{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 7 – Ensemble Learning and Random Forests**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 7._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ensembles\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voting Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–3. The law of large numbers:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure law_of_large_numbers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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ooP645QaQdiPUssQ0IMPgdDqRwuDNIWPdsJHqTZu8/fd/t5F1mSWAZhI6lggh6NatG5s2beKGG25g+vTpAWMm/7yVqwa2oOsTfwIQGWKgrFrTDNWEe9dlwc48rv7I50a0IauEHqnRDa7lxs/WNjinQqFQKBT/BCdSiNkFGIQQbaWUuz1t3YGtjThW4rP8DAdaAJmefC3hgF4I0UlK2euwMzkbf8lOp5PXh41h/wE7b2aWkfPYY379P6w72Oi5jobzzjuP8847r8Ex0xbu9W7XCDAAJVX2oMUiv1njr7xam1HcoBBTo2kCsDldXpOXQqFQKBT/NCfMnCSlrAR+AKYIIcKEEIOAs4HP6o4VQowVQiR5tjsAk4CfPN3TgdZAD8/fNOBXYMxRrUuAxSnpUuIK6HN4nHh/bmbCmW/FVeRfsuCsvYsDjnFXObBlHPsaR/369QPAGtMah/S9bdMX7Qs6fntOgIKLaoeL2Ztz/dqemrUNuzN4DhyAJ372yZhLdhcc0ZoVCoVCoTienOgQ61sAC5AHfAXcLKXcKoRI8+SCSfOMGwVsEkJUArPRhJ9nAKSUVVLK3Jo/NDNVtZQyvzELSHQWBrS1L3dhcQUalrJycrzbunAjrpISv/6bN2tyVXxVCflvvImUkuwpK8h/ZyOOQ5WNWU6jGTduHE888QRn9m6JUbi5OmQ1/Q0ZnOVaytUhq7nAvJHaxrFL3lsRMMfDPwQPz/5sRYY3ygogv9xGi4d+pcVDv5JbVu1tv+6ToL7RCoVCoVD8I5xQIUZKWSSlPEdKGSalTJNSfulpz/Tkgsn07N8npUzyjGslpXxcSumoZ84npJSXN3YN44vnc36mzwn27AMOdBJcdVxaykQVJRU+QaTk26/ruygeWvM5JT+voXyhT2uR99YGbyHJv4PbZvML6w4PD/dudzT4nG3DhZ3rBjTl9Yt7eNtmrj/AzZ+vZU16kWdfM3+N7pjI3HuHecc9NWsbXSb/QUGFVrjp4R98fj8ATaMbjpZSKBQKheKf4KQrAKkD+hf6TEfxdolBglP4SzE/mVbjEr6Xp/z3PwMn05tZIqIYMPQRLH2up+x3X+Vp6XCT+/yqw66nvKga6Q4u7BS89x47u/cg66abfevX1f+WXd49mtM7xtO7eQwAd8/YyG9bcrlg2nLereU78+pFPWidEM6Op073O77P/+YAMGe7fyTSzFsGerczCitx1bNehUKhUChOJCedEBMsMYxegrtOu004cdUWGHSByeXCx7/W4KlcZXZyX1lDxfLsoP0lh6r49JFlrJ6dHrQ//+VXAKhcssTb1q5du3rP98knn/Dss8+SUVgV0PfsbzsAOLtHEyJCtGsJCRIWXu1w4Q4zUD2mKbZTEnBb9FiNguoxTXGHGRj24gI6TPoNmzPQh0ihUCgUihPJSSfECDRn3tro3YHmJMBPiBH6wKgmoTt8pI4zz0rJT3txFlgD+ooPacLG6ln7A/qkDK7tCAkJ4fHHHyckxFerKTo62v+c9RSrBHj63K5++69f3INmMT5zUYdJv2MfkKCtIdKEfWgy/VdsB8A+OAkAa9NQzl+9mz+25mK1u1i5r5Ddh8obdBBWKBQKheJYc8Qh1kKIcEB6oo3+gwRKKzokrlrmpLhrOsNXc3HVFlJ0fy8a3W0P1FwUHgiMIALIzyonfX0u4WiuulL4y5o6nY6HHnqIuXPnsnv3bq677jqefvppb//IDone8O8xnZP4Y+shb1/d8ghn92jK2T2aUmlz0uH1+TjbRYGhftnWNiABGWViTXU1N8xej3AECi4t4kLplRbDrSPb0DohPMgsCoVCoVD8fRr9ZBZC3Ao8CDT17B8AnpdSvn2c1nZcEPWYk1wC3LipxoG9SHvo19bE2I1mr9rq3iG38vLiqfWeI2JkPOXz/MORpUdLYc/KQh8Tgz48nJU/+zQwmVsL2bzwIOmbfMcNNEeTmTqKA81GkrZtFzIrnfI//qBs9mxazf6Vbjt3MvLmmxF1Lup/Z3fmzO5NGNImHoNeR5XdSafH/2Bsl+R61xxmNuDom1Bvv/c6ony5Z5wtwzHuCgwnTy+sIr2wih/WH2T/s+MC1qdQKBQKxbGgUeYkIcQjwHPAB8Bpnr+PgOeEEEdcvPGfRKALyNJr8JiTlht28WXIEj79YwaAnybmtisu9G5vi2vJ+53rz16bfUdggjpZ7cJtt7P31NPY1advgLnolzc3+gkwAAebDOZAs5EAfPrGAbZOep2y2bMB2DduPAVvvEn5H39i3biRuy+5BJNJEzCWL1lE9wQDBr329oaaDKQ/N553Lu/tN7/TLSnz+LYkz9/g19czIpTcET34uWcbLk2JZXavtgHX5GoZgQTiw010bRoV9LVo+fBs5T+jUCgUiuNCY31ibgJukFI+KaWc6/l7ArjZ8/efIdqcGNCmA1xCsE9/yK+9tiZme5Mkv76dMWm4KzWhQ7rsWFe92+B53VYn6RddDIDDEMrbN88/7Fozmo/129/R/jIqQ/3XcfCuu0i/6GKyzz6HUzy+MYsXL+aNN97A3YBvDECPZVtpt3gzw1ftCOj7sZdWGLxfdDivdEijV1QYWwd1CRhnG9OUNY+dyi+3D+b7m7UopnN7NvUb0/6x32nx0K9+uWjq8l5WPmPW7GRNaSVZ1UdWB0qhUCgUJyeNFWISgdVB2lcBSUHa/8UEOszq3Vp0kk1oD9mQkHKGDP2MNp2W+o9L6MSSTmcAkBMWD0LgyFpJxS+34So74DdWhGhanLBTUgBwVzuxbdccZHe3briUQH1UhSWzst/j9fYnvOVv4iorazhzcIFDu94dlb6Edu91bsGM7q0xBwnljjMZyB3Rg6yh3fzax6zZyaKicl4tLWbazf1Z3zKE2Y+MCDi+y+Q/eHTmZqod/poZKSWT9hxkY7mVM9btpu/ybdyzI5NLNu4NmEOhUCgUihoaK8TsAi4N0n4psPPYLefEMjJXy5+nl+Cs5bYRHq4lh5MGf1+O0EF3MbbdWYTqBeOaNEMXGocxtb/W6aj2G1u9fAoxF7Ql6rQWAJTM3AN6Mwg9uSmn/K11u0Xwt01fR/Py2muv1TtHtStQS7N5UGfOTIxmWGywwuI+jHodOcO7e/c3lluZsHEvcwrLuHrfAXZUVvNRTiHpz43nx1sH+R37xcpMOkz6nbu2a0LKvMIylpZUBJzjy5wi5heV8+Oh4oA+hUKhUCig8ULME8DjQog5QognhRBPCCHmAI8Bk4/b6k4QeqRfiLXRpIVD2/HPDVPY4letfWgy7/WLZHe47+WTTv8QantGBmF9kr0aGYCIM9/EMvAuv3HDF955xOvNj+9Rb9/ZM3+kZcuW3v3SX2Zh2xdYX+ndrMAqDQmmwFw49SGEIHdE/ev4LFsr79AjNZr058bTKy0aCdi7xOAON/B1riakXLppHxds0DQuv/Vux5VN4vzmuWlbBu9k5tWdXqFQKBSKxgkxUsofgP5ALnAGcJZnu5+U8sfjtrrjgs+cVCO31JiTamjTRrOcOfCvAl3Q7lukcFJh0l62SwaF4XB5NDAuzY/DVearbL1/wkVY12/wm8OQ0J6E/PUAXHRHW3TSycgFtzJw+aP02PhmwGr7jm/BkCX3MXjpg962rZ2vI3/yTPQt23jbmrz4AgAhNhtXXXWVtz37/vvZN248rnL/cO4lJdr+dz1a81efdg0KJA0xp0/9yfeS52+gwK6ZrH64ZRAP3dIXd9NQ7IOCWyC7hlt4vl2zgPYn92bTfvFmkudvIHn+Bu7cnkm5chZWKBSKk55GJ7uTUq6VUl4upewtpezl2V5/PBd3vGhi1UwpXUq1B6EWYi3Q6ZykpW30jnMSqJnYder1fvunjPOFJSfc1A1LW1+l6+pNm8i4NIgVLiSKqJI95J93mq/JVkJs8Q5GLriVy8f7UvD0O7MVRqcVk6OCEQtu9bZvXphN6S2vacd260bUmWdi6dnT2z9yyFAAHAYtir7www/9lrC4WDPhDIgKp2tEaOAaG0lHt54D3dqTM7w7o2IjARgW4zNHdVm6Bel0M+f1VTy51z9z8ZNNEkk2GTktLpJZvdpi0AmEEBwY1p2sYd39BKvSWkLLjNwiBq3cftRrVigUCsX/D+rNEyOEiJVSFtVsNzRJzbj/AhLoVObm0T9LOUdqMpwezSemabNtNG/hK37oCCLE1EeTF57H3CIKc6umAX2uor3oY1v7GkwRmGo5Aic/8QTO0jJKvp5HaM8Uos48k3PMGZSXaQUZ0z78gMxrryNl8uOw0DdNVYWTvjt8D3Prek2mrN6+naoXnodTTqEsMpK4oiLyp01nrXEYfQYbCYn3zWHQBc/hYt2yFUNsDG6bDXMt81RtCj5aQ/VOzYxmahHJ5zd2QwhBxeZ82uDT/Ez5ch3vdDMFHF+ZUU6rPSW8cmUb4sPNQdeUO6IHPZdtJcfmX/8zz+5kamYet6YFRpudzNz59Xp+2qAJi92aRfHzbYP9+rNLrAx8bh7JkSEsuH84ZoOOT5alo9cJxnZN8XsfFAqF4t9OQ8nu8oUQKVLKPKCAYGE9niz+aHLAfwOhXUZcmZulrc0MznN4zUl6nX8IcF2fGDc6dAQPW9ZHaXlSIseNo/CTT7Bt8wkXVYteIOIcXwj2wKQm7I9Ogh2fgduJLiIc24YmWPp3wNInHulwI+dlEg5wQTvCBg6kY42wsnCed54tCw/ScWAKsSlhGEx6oi+5mJKvvibjytuI6X8RkM2c007l8tWFWHUh5G8p4pfN8MPIbIjvwoSkmKDXIt1u0i+4wK+t1exfMbdqBYCrogJ3RYVXgAGwp5dx8GFfjadVQL8xmkbmnVT/j9miOeVU6QWX2g9ShVZ4ct2kU4m2GBEC9uRVEGkxkhBuRqcTrB/YmXy7gzC9nlC9jlGrd7C1opqn9mbz1N5sVp/SiWZm40mdVC+/3MbyfYVeAQZg04FSWjz0Kw+P7eCtnVVDblk1HSb97tc26aetzLhhAH9tO0RMmIlrB7XEYvqHv9pSwpu9Ib4tXDrjn12LQqH419GQEDMSKKq1/bdLF3s0OjUJ8wqAh6WUXwYZd7VnXG1v2TOklAuEEGbgbWA0EAvsAR6RUv52JGuxVf6GWX8+AHopg9ZO2oB/cjg7JkKoDhjnFgI8IcnCaKTld9+xo1PnWiMCX7qWZj2c+SbO/B1Yt/tqF0mnoPT3wFpKNXQfncrGOVne/W+fXQPArdNGEnPhhZR89TVho57AJayA54HW9VwsOiNpoTouOyUUl05TrJ2+Yzm73OW069KFwvffJ++llwEI7ds34Lz7xo33ClLpF07Avn8/Yac9hy40uJJOB8xaWMEZw3xlB76otNBhbyWmVtGE7i7hTyI5g3JKkPR66i9O65TE+qwS8stt3mOuHdSSx8/s5Od0/GPPtrRdvNm733f5NgAODu+O/v+xIFPtcFFQYUNKSI0N5a6v1xNqNvDlyky/cR9f05cm0RZOe3URQIAA0xAXTV/h3X7xDy3wcNuUMRSU23FJyaMzN+N0SValF/HpNX3o2zIOi+nvleTw4naDdEP6Img1AuZOgSVaEVSK9sL8Z6DXlRASBdYSiE5t3LyOasjbCk17H36sQqH4T1Hv3UdKubDW9oJjdL6pgB0tt0wP4FchxEYp5dYgY5dLKQcHaTcAWcAwIBMYB3wjhOgqpUxv7EKkqxDhCVV2uDVzks6lqdI30R03OnaJjgBcJd/nE3E9NkyYCEzEtvvq6+nYv793X+h06KOjcZWUHHYdhoQOOGsl6rVuCIwaqs2g89pwytmtmXb7goC+kE6dvNsR0lcg8vOQRUwbdk7A+FWbdrBq0w4e69DBK8AAVK0OlhLIU5TS7ca+fz+6mJboQmOx7ZgFQoexxVB0Zk1gESEGjCmhJO8v8woypxcdYtT5YwCtjlT248sAmEUEIynDDvy57VDAOT9cup/bRrYhNsxnjoow6FkxoCNnrdtNnt2nPWu6YCNL+3egdWhIwDz/VgorbMSGmerVItmdbgY+N49mMRY2ZJV42+85tR0/bgisjj60XQLD22smtu1TTqfj45q2pWNKJHod3D6yLaM7JiGAN+btpkm0hQl9NGHg5s/X8tuW3IA5Oz3+h3c7QZRTIkOJEnbmffUO8wJGQ2pqKmeddRYJCQm43JL8chsDnp1Lj9RoHhvfkdyyakZ1SPJpeeZOgcUvB5mpDguf1/5quPgr6DCu/vFSwpPR/m2PF0EjCrcqFIr/Bo36CSWEcAE1pqXa7XFAnpTysHcFIUQYcD7QRUpZASwRQvwMXAE0unSBp/DkE7WaZgkh9gO9gfRGz4PEYdQeHFVOiVsnkG7t1/7zwj+hnAlNM2DHjC2IEBN1/fXoTP4+H40RYBqD2+5CV0ulL3QCvU5w09ThTLt1gbe95FAVkfEhtFm6lkMvrkUgGGvvyW+m4L7XvTJ86X3mvf4Gnc6ZDkDhb3disFWzePBLDLiwA517hpP3wouUzZqFfX86hR9+AEDYsIcBkM5SHHsWYt/+I4a0gZiaxtL8oxeRLhcZE98nObETc269DJ3bTVWrLwnt2ROdSY8xJQxHjubAPJdIrqeCVuh5FAu3U4lIi2BdpvYa/rYlh8v6N/dbfwuLmU2DuvBXQSlT9mazu0p7j94/UMCzQSKcTjTl1Q7eWbCXm4e3xupwkeDxNXFL0OsEy/YUcONnayn3ZDHe+8w49B5foEqbE4fLjVGv4+LpKyiosFFQYfOb/5W/dvntf3BVH0Z19I/6spj0pD9Xf3mMu0b7R5bVLUtRUGGjz//mePd7GA7SwxAoONUlKyuLqVOn8nF1H2oXXN2QVcIF05Z79yNCDGyamIBoSIDpez2MfREOroUPRvv3fX0J3LwcEjtq0YHF6TD/aWgxBGzlMPfJwPmmeDSH538Ah7bC3rlw1SwIiTzsdSkUin8fjdUD16ejN0OQp3pw2gEuKWXtu+9GNI1KMHoKIQrQTFqfAc9KKQPy1gshkjxzB9PmNIBkf7yJ9dvLKXHoPS3BL9PtCeIqIi6oJibYj+iIMWMo/+MPnHozq/s8zAgpMR6FqSP78WUk3d0LY1IYUkrKFx7A0jkO6+YCzjk9jR9/10wJq55ZRYlLMjDc95auTf8a2rUP0MJcuvJPIqurvPvLysvoBJQLKzPPPQuAhNwQls5Mp8eYkVgGDKBs1iz2jdN+9Rpb+bLxtvjidQxRZlwVlezq0wdHJnx802DsDsHgFW+hM4QgpA67KYKMSy71mqSS7uyFPbuCvDfWI4AP8JmdpnVrTtKlHbE73bR77DeemrUtQIip4dT4KE6Nj2JvVTWDV+4g2/bPlizILrGSHBnCWW8tZX9BJW8vaFzW4fk78hjdKYkpv2zjw6X1mxN/u3MIN362lsyiKkZ3TOL8Xk3p1TyGpMhjr32KDzezffJIduzYwU8//eTXZ9cb6Ny1Kw9VxtEmO4tDhSGAZEKIzzH+6pA1zLZ1oEiGehzn3H5+Zjc4v0S89yMAl9ofYbO7Fe+aXmFa/CO0LF7OpeefR4sOPbXs0al94YlSsFVASQZkLIPZ98E7QRJHbvNfK3dthrAEeLpWAdTvr/NtP+cxS5mj4MH9SlOjUPyHaFCIEULc49mUwE1CiNqpVfXAEKCxBvdwoLROWykQLD3sIqALkAF0BmYATuDZOuszAl8An0gpg65DCHEDcANAdK1f6FJKwowZ5BisCKm1O+uJOF/GEABe4DEMaHLUdcXwgccv1hok+23Tl17E9fgkfntiNlZHAvPLnUTqBAPCg7/kMee3pfj73UH7Sn7ZR8L1XXFXOij7PZ2y39O9fRNu6Yrty8BL31++GaurnDKDz3m3W94uHt4Ry0KpCTB9HW1YbdwDwPshc4Oee+X0zeRtiaW41wP0XefJRdPtEgDC+iZjiNI0DPrwMAAK47pQSQQYYf7Q1+mx8U02dL/dO9+qa2eQNqo7g85vg6lJuJ9GpgbHpgLkxRKTwfN+ONyMeeg34tvEsHRPIWsfG01cuBnpcGHbX4a5ZRStQ0O4IDmG+YXl/JZfQqvQENqHHf7Bvi27jHFvLNZegyv7MLqTps3IKqois6iKlKgQWiWEBz12a3YpcWFm1mcWc/MX6wDti+JqGorOateEW53QGt3+IrLUCzDq6Jccyer0Yq7/dE29a/z02n4MbecL5V/0QGBJh4bIy8sjIyODvkF8nXJzc0lMTERXp8yElJL33nuP7GxN81IaEsZX/U8NnDwKtjfpSO7QzmSWOFid3o8Hvl3PFSFrcep0xHTUMyJrC+NC5rAmtgv3ZHxC9Vnvsmr2JwxzaibFP1x9WObW6nJdYn8MWWliXsJpfPx1Hjh/975uH13dl7ZJ4STFd8SY1BnKcxo0Qy28fDFxSW3oUpNC4IlSqMiDH2+GPXMCD7CVapqaB9PBEgNVRVCeC9FpYK71GZAS3E5IXwKfneM/R7O+cGC15tNz5Y/wziDI3wmP5oL+GPkOHQnpS+Cvx+GqX6BwD6R0P/wxCsV/CFG3mrJfp2amAWgOHABqZxizo5lvHpdSrjzsiYToCSyVUobWarsXGC6lPPMwx14M3C+l7F2rTQd8CUQCZ0spHfUdX0NM+1T5x+NDabL5Jn478AGtrtUeXh9u+oy53UOZnP4s7Zqv4TLxvfeY6376mj1nJbBQjPKb66VsQUGvIp7LjcEkBJnDg98c3r55HrVf4rOjtV+ioYOaQLWLqrWaH0jS3b0o+m43jiwtNDnqzFaU/uLLtBsxIpXy+T6H3pophV6AK/A9nLH/eXRhY3j+iiHethsW/uSt4d3P0YZurub1Ci/hpW2xWFO8691V7aLp5g8whCVg7nQuAPGTBlBd6SAiLoSsbUXsePxV9iaNCjpfXSRuJjzcj61LsolNCqWJw4VtYa36UzpIvrcPQ95dxrdlmqnuMapYiJOLMPH0lJHkePxqTC0jSbyxO+9l5TNpz8GAc+0Z0pVwQ/Bf1y0e+vWwa02OCuFAmA5dlZMxY1rxe2kFz0XE8ui3m6geo4XUG9cUgABXcijupsHz7uiAAXoTa2fvxzbGF4pv3luO2OOrc/XShd159a9d5JVXs27SqUSEBIb679y5k4ULF3LeeecRHx/v17d27Vp++eUX7rvvPhYvXszKlf5fz6uuuori4mLWrl3LwYO+1ysyMhKXy8XYsWNZv349e/dqWqSM2CR+69pwqYw79v/ALefcSHRsUxYVlTOhnrpX1x/4jrEHl5BWncPi5F58kHQVQ8JbcNeQ1pRVOui/aAsyOjAcH6sTQ1Ylhv2+31EX9UllbPgu/tpTQrPW3bhxaBsqTZF8mlPEU3sDzV4Tm8Vze1oSiWYjyzN2Yg1LxmGv4jR5CJHS1WdqOl5M+BTangZGjyN/1mrY8AXsmAWVtXzhznsP2owGUzgYar0W237WhKmQaM2EZrT4Tc/aj+GXRmQAv+gLSBsAYfGHH6tQHCVCiLVSyj7H/TwNCTG1FjMfOE9KedSFbDw+McVAZynlbk/bp0C2lLJBnxghxEXAg1LKXp59AXwItADGSSmtDRzupbYQM/vA+7S+VgsJ/njDZ/zVM5RJ+5+nQ4tVfkLMxJlf0eacFTwoXveb631LDOlVt/E/8RRAvRlvp97k7/o4IsJApF6Q/Gh/DBEmDjykCVJNnxkMbomrxAYGgSE6hLI5GZTNyQyYUwJnDQ1jbLaDW/YEmk/eL/iAkvBwvj7bl5jv8uW/E273RVZdVT0MIwa+MS2jTBf85Us+NIQzooI8UNCEmu3Vmgaq56lprP8rcJ31URm+n6pwTSBLyB3qbR9wTis6t4yk4KPDWwZLTIJou/9nN+PSNpxfGOgcXMOy/h1pFWqmoMKGxagn1KSn5cOzD3suZ8twnO2iAtoN20pwdoo+7PGNwXSwCrGnjNTTm7PHauOPPu2IMxqQUhLldmExGXn11z+4pH9vPng3eMV0vV7PxIkTmTZt2jFZk0sIrrnuegbu9j1g2xkFN/z2B4fK+hLikNj18PwFx+bhHyZdVIqGTTnGdYUIqxNHlxhkPZ/Nw9E7MpS1ZVV+bS+3T+WU6HAGepIo5iwcVq8N3Y+z3oIO42H1B1BxCFa/B8MfhgXPcQwCOk8sNyyAJj0PO0yhaAwnSohplH5TSnlk+uvgc1QKIX4ApgghrkeLTjobGFh3rBBiLLBOSnlICNEBmAR8W2vIO0BHYHRjBZhaK6nzH3QeQc4V5AaamdyEnvtt0Mq/vTixAF168JwxtRE6gXT7zrWi0kmKQUezCO0G3Ow5n6YEncAQ7/t1FTm6eVAh5lCIIMei48PWZq8Q80vm2/SOH8Pu8g1Mu/KugGNqCzAARs9bP9bRkxnmZRhL7ehMTRmna8lM8yoAcpMWs8zZjIHO9gDkizKKdRW0czVhR7Xv2usKMK17JTDqqk5UlthY9PVOSg5ZueChPhiEi+n3L/MKMACV4elUhWditiaw4kfo9c4IhEmHtAe+tvt1eezV5xIuQ+hnb0PdhNPNv9zDY02NLLJXs6aoipAkC0Vdfea0gSu3Y/7joPfhNKazZjo6u0cTHh7bkQHPalqpmndr1SOj6Pfs3KACDFCvAHN38yR6RYZyxeb93NcimTiTgYd3HSBcr6OilunxjY5p3LFde+3sTUOhaSh7rJoD75g1uwInjmrGKzsOcUZ0As1KAqPYXC5XUAHm9NNPJykpiXXr1rF582a/vuv5kmVpt7P/YD5Wl/Z6uoHpHj+q9zwCTM+91ZyxRnvwl9KHEM+rZHLByI1VzOvur30auN3KiM1WdBIyThOkGa3Myy1kWZuuQV8zwCvAnLtuAUnlJQB0GTKU23Q+IcnRKy7YoQH02FTKtJEdkaU2XgixcSCjlNWx2vx1BRiAe3dm+e2nDFvI2yE6xsUlobdnUvi7xFlgI/HKBHSJqZT8mk719iJi9R0wlplxp07E3Coaxr+kTTC8zu+yTd/ADxODLzapKwy5R9OM6M3w4WnBxzWG0U9Au9M1DU/va7Q5QzyfX2sx/Hw7bP8l+LHTh8Ogu6DfDRAVmLTTD7dHKa/8hxT/MI020goh2gEXAGngX1RISnltI6e5BU2DkgcUAjdLKbcKIdKAbUAnKWUmMAr4WAgRDhwCPgee8ayjOXAjYANya4Wm3iil/KKx11NbAyUd2UBb3EFy9rl0epLfccGL/u070qczHO1BMzI2uM8EQHyzcPIzfdlrrW7oc1/j81WE9k7ympxqqJ17pUoPJeV7qXKVs/jQdyztMzJgjnvffZyq1l2QRt/b9mNxBefEhBMhLVxVPYzvs1/GEjOaLJPQ3LU9bDMcoE+TLpRnFfGTWQu93uewI6n/Jjf6mk4YjHqik0I5607tl92+ffv49NNP6d27GQW1LD5V4dpD3GbJR5a2x1HtoumUQVRtyKN8XS7lu/IJwUTCY314/yVfeO0Wg/bQ2Vt9Ck9gwewRTc456OAc9EAEZMMYZxHFaWG44zzRQfFm4g7Zae7UsW5THvEIppzdhRt2ZnpNQzU8vi0L4+im2IAIvY4bEmNZU1KJbUsRK1prwmaS1c3MRaX8EpFJJ3sCqc5wnJcY+O7Td8l85BFMnqi1a5pqqvt3MvP4dHc6I5f/xbmDH+Dstq15evN+poccXiCuYVb3QdyyJZdrD4aRpSsgK62Kbbn+/lSPPfYYL730Eueeey7t22tCaEtjIedv9v+qOqWJ/FWdiRZWwqWFonAd08ZHB5xz/JrAB//Nb49ApxNMrHby5JNLcesELh0klbgIcfi+X83/lEAII2lCr72LCa9MZXPLKkLch4irKEUHFIVG8E3fUXQ+uM8rwABsWbyIByIjuXniTVyxO5slpT5z0qBQC/ucTn7q1YbMKht7lx9kxJICj2irgy+0CLxHa615QYKB+3pZeH9lFT1KXFTp4cyhYZR66qGdm2VnZqr2nt1S7YaDOXy/2EbzKu16Mj/PJy+kAJtO0EwPRV8F+qNFjEhF6EWdHyAppDySh27v9xRHDSe2aQQYLEihp+jL7Vic8ZT9kIkz30rEqE2YUsIQFgOlv+wjZmw89gI9pX9lorPocRXbSDjfginvGwp2nUZIUjkRI1ojE7sinS50ZgOc9r+AdWGJwXXhZ4x/YzHdm4Rxa6dqkg4txtTlTETmcvj1Xlj6mvbXfDD0ux5yNmmO1KMma75BbqfmY7Pibd+850yDjmdqEWFF+5B/PAxnvoEoPUBhaEtiPxqofTt7Xg7mSNjyPVQcYs/Id3kpoy2/b9XC+i/pl8rajGLeurQXzeNCmbZgHyFGHS3iwxjePgG7081Nn6/F7nRz64g2dG0aRZzKMP23kVL+pxOFNtacNB74HliPFsq8GmiN9rhbLKU863gu8lgR0z5V/vRSKq2WPs+srHdpe50W7vnd3suY2eY8Hkh/ne7NF/mZk67+5VsmzP2Vca997DfXm3IisRRxD2/R0VjE1E5pxMUN8Rvjdrl5xxMG3XFgCtuX5QBw+VMDiEpoXL0iR24lh17TnEb/TDYQb5Pc0M937E3ffUBEQTogMUVexdOXtvU7fnHfdvx83QRcphCqm7TEbQkjdP829NVVxJiSOK3p1czMeB27u5o72q/AJiOxd/2N3Rt2+IVnh9qjqTKVePebRXXg6tsu5N3btXRClggj1RUOrnpuEGFR/jcWt9vNlClT/Npa7d3Lvtat/dr0jlBaR/XHnrifsWPH8s477xz29Ul3xrPM0RID8KfQwmTdHi2BrpZBYGBoFfYhmublwe+LcOkEL50bPGNxMH6ZU0KKSxNynSPNbEhI4rkDBXy1rJJPzMGypfiYNGkSer2eH3/8kQ0bNvj19XC2YIMhnQpTCNnR8bTL0/yCDkXEEF1VjhSClW37sT0xgZt3VfFXspE9kZqPTLLVzaxFmlN002usiOb9wRQBBbuQoXEUfLANW46O+Lg3sMpRRFS8QpX7FHZYJhJrrSTEGYqUki9sLj4cHUlZmL8Q/+D3RexNNtLuoIMrWzyFZciV6PpcicGoC7jpVZbYcDndRMZbwF4F0gXmCNI3FfDr27Uilp4bRGiUiV+mrmfzwYUgJL1bjyIjPZOUlGSyNpXjFk4qInejcxuxhvl8W9wI8iOi6XIwHmmNx24qxWSL48Lbu2P/bBviMAagK6mgpUvHOKuJ/kEc7AtxU4wkRgrmppl5oZPPMTzK7vYKOrV5boOV0YcCgiYPy2+lDjqE6LSkl8cBxx3daR4Tis7iu859+RWMfHlh0PE906L5bkIK+reOjTnpIEm8R2DdODM27uBDQBDmyWU6z9Wd552XsFOmAZI4UUUvwwEihI1triSK3RbKpZkqggssozsm8uYlvYJnl5YyePjoiUZK2DsP4lqD0w4bPtdC/PfMgZiWUOxxPzVHagJo1wvAFHbcllMTGRoMS9d4Yie0AyEQhsDPfGP5t/nErAW+k1I+K4QoB7qjpYP9DC0p3SvHd5nHhpj2qfK7d0w0W3M/8zcupu11WnbSBYziPXELb8gbiKPQT4j58b4bCK+qYPTb/omFv5Batt+HeYl48rmX5xk10t+ZcebL68jeXQJoGXU/f3w5pXlWbp0WqC2p4fot+1leUsnWwV3YWF7FoWoHXd7bQXi/ZNrp63dJuvnT5ygaPJQZrfytc/uHdiPkx5t45Zvg+T16jDkDa3kZO5ctIsZUxWUtNmDSCXJsb7PWsJe1hvpDlidPnkxRdiUGky6oUCalxOl0MmvWLDZu3OjXd+XadfyW2oyW+/Yz4psZPP9K4z5Cpw4+g+Wzt1MR6XutI4s7I9x6ZpjN/PrEaN58SQtii3RbGOvoSYS0kPi/gaQt3lzftACk5ju4YFkFhyIlX47wmTCu3mfjtt2Br8Maw142GNIbte677rqL1157rVFjT7N3Z4f+ICMdXTCgJ2JkKpGjmnPwUc2Ha8KgUPaFazdsISWyzk36r6U/EVMR+Bm7o5eFZQm+h1q03U1JkAczwKIlN9DGuZuyUe8Tfcq4QCfSWhT/uIfKFTmEdI4j7vKOCCFwVToo/HQb9owyLD0SqIgPRT8ng8gxzYkckQaAo6Qad4mN0tn7sdfSVmbZ3RQ5JRVuSbLRzfyoxY163S6wDaCs2kKuTpA6MpVVv9Qfqg5a3giLDqrcMP7WbiS3iuLD+5d4zb8SmDkgjK3ND/9rX7glw/44RHSihUcKdOiA5+1WIl2CVnY9p0UZ+aK5EQF8n2ri0+WV6F12KkU18VITvg/a3Zh1EF/rwZHldJPq2Z8lq4g25GNzxTMEMzoEhiOo+FKMm5k4mBUGcZFmtueUUlM5ZpRxN6l6X/BomKxkovgKpJtZjOIMFrCEvoRKcEjBmyGXsKU8hLbiIH+ZH6CYSNzoEEjeoLGK+SNjryuW1n2Gs+tQJVvTc7G5BdV+pWEkzw9w0KpqE+2yf0JWFRLtLgFgqvk6zpj4JIlLf6Vwub/GVRduJPme3giTHmHQ/S3NRPWeEgre991nIjoWEbnveoTQ7h/lznMpdV7nd4yOEtxE+7WZxBYcshUW3TKijdPQmQ24blhP1V4XtIjEmlmG8/cMhF4gnW6kG/SRJsL6JOEssCLtLsxtYzC3jMIQG0JuaTWXvreCUaVurnI0viagId6Cs+AIPTY8pD4/9F8lxFQA3aSU+4QQRcBQKeUWIURX4FcpZdrxXuixoEaIMVU0ZcuMZrS7TovaWMgIpovbeFXeTCJ5fkLM/Ju1cOIR73zlN1eNEPMET2PCziM8GSDETL1pHqUWHXopefC1EbicbtxuibG+ejRl2SSv1fIJ5g7rRvJC7Rfs3L7tca5ewhh8Ybbt9m5hV+suQaeJtZdQZIrW5hneHT4ez+5cyc8btPN2HjaKrQs1/49hV1xHaGQUv03VhIhmoSU0Cy1jRUEaUuio6NDLb27h1iN1Lr+2yZMnI4Rg1apVzJ49m3vvvZf33nuPsrIy6uOJJ55gewctI7Iwm/n63HPqHQuQmJjI1VdfzUf3+NLi5ycvChhnqWyGNeyAX9tQeyeqhZ05MRa+7N064BiA/nu3M3DJTxismrnCOOQx5iUZWBFvYPGcciz+l0wF1XwdstS7H3JwHzp7NSkk0q3JOKJlGD/Yf6O0Tv6WXp16MKbPCDKmr+bLEF+tqfNt/Vlh2M0gZwcSOzXDvr+UmPPaYuniiSCxlSMNYZT8vJeIYal8a63g7l3+11mXkbkO5iUbCXVKmlW52RXZ8APvu4h42rWKJTG28VXNaxzTaxBmPdLmqmf00aPrGEluKzd7/ljLNkP91x1R0h5zdWKDWpn2A5LJ2l5EVWn9Aro51ICtStOwFIXpmHpGNGesrqT7fhs6zy1zR1Mj3w4OliECBuzOZND2cqSMZFvLXH7vFhjeXptT9m6mODSCoTu3EpffE4Pb/3PjFnYKk1bUc7TG59W9cKLnZULpX8tTwI3Ejdsr8OSIYn41r2twrsPhBqpMFsLtVsrNFsxOB7mRsYQ6bMzsOZRBezbTJaIZX2xzkBobys+3DeLB7zeTs3UFXQz1O9/XMJA1JJHPTMZqGQqEDr0MNLkaDAbaV/anJXl0MbxLues8bO7e6CjGTQxh+t/QUUy5K1Ar1BhMzSMxROlx7VxOtHyTCtdYDKF2Qu0zEVRR5HgQh7sVZt0mqtz1/zj9N/I4VSzGSd2w3rEYeZT6f7Q0ln+bEJMDjJJSbhNCbAUelVL+6AmbXiSlDP5N/pdRI8QIl4mdH/Wg3fWaA+sShvKOuJOX5W0kk3NEQsxzTMKKhSd5JKgQ89RF2i/6+qKX/HgiiuRhgereWD1c9NFzvHOlz1nw6jAdH1cG96OIcFaQbCsgw9KETPevmo07uRsZA9/EEhlJYotWvHzRGQCMvOZGmrTryOcP3xV0rh5xueT3vBxdVBNOPfVUQkJCeP755/3GnHPOOXTq1Ilnnnmmwct7/PHHOXjwIKmpWnIx29697BuvrUMC31x8kd/4qMgoQtI706xDPGfc1JODu4r56bUNALToFs/+zYcoSFrKkbAurR2rWmrlGc5dtxC920VcZZn3kdd0fxYCA6enXA7AhqL5bDUcwJrYhNCqGMzFkURG7yIjzPfrPGzXBnQu7YEXnZTCaWFXIKQ2Y90HxnXVI/0esAk3d8caUgVlLly4iGubBu+PBpcDblwIy96CPz1eHdf8BlHNyDto59s3M9mfaODzEZGkFjgJcbgZtN/OvO6hZIbVrwIev7qSHvttfDIyggPx2i+ya/bZuLWOpkkYdTR5UtPqiXoqnVeuzq03t5H3tRmQQuWKnAbHAMRc0A5zy0iqNhf45UECSLytB6Zm2i1GSonD4cCVWUHRL3v5s2gV0eGR7Ba5VFX55xuKi06g0OMAfda481g4fwkJydE4pZ0JEyag0+nYsWMHFlsScz7SopKadYpiwEXNSEpKwu2WbPA4rS+fGTxk3C1ACnjmwsZFaOncbsJsVsotDZsK4spL6L7nEF0PFWCz5FMaEsa3fUbg9OSaGbt5Oc2LNGFAoulTXEJH6pCRRBw6QL8eAykCfp3xoXfOhoSBWHcEox1dMGHGiYvFhq0c1Bd5+x06PU2c4RSJciqMBj4Z1EC5h1rcnpaI3S3ZUVnNwmJN27Z3aFfC9JpAlVtWjsEcwmlrd5HtqVSvB55OMfJQjv/jNaqqnChrJW0PZdEm/+Bho8euqB6KmbpaBxcL9D/S35hBrH4d0c4yyp1XUOG60G+URFIiqsjRFdPB1QQdPqf3wxlYUsxXAU5KXH0oc12EUTbx67+RSoo9eqsu6CnCzSpc3mrKOqALepIRPIQFU5ArFVQTZ5yCTpRjd3fCIHKodI2i2t0Lo8jETYyWs6rOuS26ReQMbsWPh8xYCjbRumwlbnSsa3cXt5wxiBlrDvD6XO073Q094zEyO1JQVe0kQggOCMkz53dE76xixa4DnFPyJRElu2hatQkZEo174B0YMpciRz2DrmnHf5UQ8yMwW0o5XQjxAlr5gE+Bc9HKDvwNd/oTR20hZsdHPWjvEWKWM5C3xL08L++kGQeOSIh5lftZIwZwt3yeB0Z8jqgV4XQkQkx2tZ15H1/Dfe3vb9S17BzchfZLtgTt27/4VPTSjRsdIbLWw+kJn7q4KPsg8z6axhl3PUhIWDgrvv+apd98Xu/57j4rDN2570BoLE6nk//9L4jjYD2EhYVx8803Ex4e6ABdo40B7Qucm9SeJUP64zbYiC7oidEZKB+Pv6UbLbppGoqls7aStb2IZqfomDPnL985y1oRqUsiJ3x5wPGg3ZSN7oY1Bnf3iWZhvp51GYX1jgnfvobeY89i3W8/M/DCyzjlgktwVznInuL71WzDgQs3oXVs+hG3t+X9e673awsz2GkfWca6onjMUTfRPnQ1O61D/cY0RtX9xhlRlIbpCdEJqj3mkev+LKWtW8859/TC5XQT11R7P6TLzcFHj0wYjBiZSvk8zbk65eF+6KPM2HMqyXtdE9jir+9KSJto73i3zYXOrEe6JNU7Cin8bDtJ9/TGmFi/1sdVZkcYBLrQxqm/v/32W7ZuPcLE3R7Gjj6LLWt3k1Xsqz7foUMHunXrRkVFBRUVFQwdOhSDwYDL6WbnylyatoshIs7Mm2++SXGxz9SbHpfM710G+M1/mbOMCZFmfvvtNwYNGkR4eDhde/SkQqfnru2ZLCoup/Gu3Q0TXVWOkJKxW1YQWV1FtcFIenwKC9r7tKqPrzvElF5J9c5xdXQUvZfm0QEjp3Y+Mr+IoXkOFiU23mRxrAivrqIixP/zFFdRSqv8bNofysTkdCCkZH98E+Z1DB5cMSxvNa5SE1aTmY3N2niFxrpcv2EbvatSybfaWR4F+1qFUeyo4Oy8PyixhbHA2Z3yUhOtUsLJz6nku5EdaTs0FV2INt+3a7K4/7tNnN2jCU+d04UQg46c0mpe+GMnszblIMMMuNLCcKWFo8u1os+qxNguiv7JFi7b9yedWrRANumF69A2fslPJKfMyZ+bMomgigRRSlvdAW6xzMEgHTTRl6G3180zCw5pQoeLSnc0E3q+wIaYNgBEuRw8ufMVoiqLKMnrTlHF6YSat3BJ1HN81fRUfo8bwqiMfURbSqg40INwVzmWaif5jnaYRCUSgdWi48aXL/1XCTGtgHAp5SYhRCjwMjAI2AXc44ko+tfjE2KMbP+oBx2u16JtVtOf18QDPCPvoTkZXCa/0zKtogkx1496gL0X+Du81Qgxb3MHS4VWOSFrcCuMxkjsbjdvrs/C9P5enp7QOCFm1LINbLU1OMTLy841XHbq9SwvqWDZzEd4sYVmgx6Xv4gPz78FcjbA+3WSzh2uWB7w+9uvsXWhlsl00ITL/YSaC9M2kRbm+SJ4hKGioiLeeOONeuezWCw8+OCDDZ6z6PMvqJg3j9Tp75L3yqsUffghhdHtWNt7IgZX8AdcfT5FVquVrVu30rNnL/LSy0lpGY7tjykcWPY7y8quJjzCQoo7mt9NG7zHXFc9EomkUtiYYV7W4FprE+8u41bxgb/PYJcL4OypWh2fkkz2v3oAowjju/RXiDLF0yyuA4POO429u1bx558zD3sOnbE1xrCzsJW8GtBnCB1NWsgcdG2GsmfVIvTmnhhDtUwI0UmhXPbkAJxuiaEeLUpdpJRIuwvHoSqsmwqoWBKYNDAYMee1Jaxf8uEHniCklJSVlZGZmcn3339Ply6ayXXLFk3g1+v1uFxHb+669tpradasGRUVFUydOhWbzfelHTx4MEOGDMFoNKLT6ci1OTAJiDU1/oFe5XTy4saveKcsMBS9q9zAA9Gr2RM2gSezg4f9nyhuSU3kseYJlK1ZS2S3rrjtdnQhIbidYN1YROmsfSyL1/NYNwtOAX2KnDy61ca7bUz8kBqY3+exLdUMzncyL8lARpiOGc1NPLS1mvMPOKjSQ45Fx+xTk9hvgsUlFUFW9O9mQnIM97RIponZSLnTTUa1jdczDvFHQf0m9yNBByzu14YmulJCQpp4f+hUudysKC5neWklIYCtYBtvVGk/AI0OX/3Aw9E2u4rdTer/wdFa7iKbppzCElqzl0+4jqxRA06IEKPdvBr4QwvDHgfEHW7sv/0P7cd+0L+Iex6T7849Vf41t5WMuOexescB8pM1T8g5c1vJOXNbyci2afWOSx18tkyat14mzVsv16xZ0+CcsdO+8I61jD+v3nGdm4ZI9+RIKSsLpXS5Gpzz3XNipJwcKeXkSPnuK083OLaGqvIy2bNHj3rHTexl9I493DWtWbPGO3bixIn1juvVq5eUUkq3yyW3te/Q4JxXjblfSnuVlMUZ8t1bRjZ8TZ5rl5MjZa8UXb3jzusyUn50xUS5//GODa4TkBMnTpRv3PSHfOvGuXJQh7H1X1OKTsrJkTL7gSbypQnjG5zzudOiZcWjcfL3m8+WF/Tu2uDYlyaM9/41jYlscJ3H+n3q3qqzLJq5Wx58eoXMenBRw5+9d9/1zvnuu+826rMnpZS9evU6odfUs2dP+f7778vJkyfLyZMnNzjnGWec4R13xhlnHJNrGjc+QmZnfyfnzG0l336naYNzvv1OU+99Z9z4iHrHGdp29N5Lkuatb3DOu+6OlzPndpNz5raSd96d0OBYu80hy3Jy5bb2HWQns7necRdGRcltXXrJ3ePvkL9e9V6Dc/561Xsy68FFMuvBRfLS7mfWO67mHuFyOeScua0bnPOxlKZyYb9T5MZOneUlfU9pcOzkyZPlQ1P+J6fc94BMaFr/69+zew/56SWXyu/PPkfect7FDc7Z4uV3G3UvP5L3KeKex+SAGb/Kpn+tPuzzKWneepk2d5lMmrdeGtp2rHecZfx53nMPeueRBueseT41n7tMdhrfvVHXBKw5Ec/1w+aJkVI6PUnqOqDldvl/iwsDrkakzulpLqSmnHcV9du2C6L0HOvauGa3HYEJXmgJIx5teHDfa8H+nrZtaVw4sSU8AqE7jPq4JAuiUxs135EgdDpC+/eHnfWX4zql1yFfIb/Mwxd7lG6wFhkhpQfkBHdmPFi1mwlpGwntehY3nnUj7733Xr3z3XjjjRSsN7NnTR7ysNZxSLFUcG/HxdzXwJjoFp34uEDL/GwInQXUH0V1/Zvv8/7t19fbX0P2zu38/varbF04lwNFgarkurjdh38t9dFmYs5pQ8w5mtqZ5xse/19ASictWj5Dy1ZGOnT4H08GKXxdQ4cO7dDu1cc2ZHfb9gcaNa5zp9dwOBvWbAK0ZC/vyPPJJZklDKOh/M0JEQ9gzrVD8ocIZIPzLlqi5RvibXDcCOwJPi63eU92T4wlrNMsMnZ9D5/UP+fCcif7Qxwg3OSUZ9Q7riJ3K3PnBXfKD0DoCUuoouR6G6nfNVyA9Zx1X2HaJRAIvi4sJDCFpEab9P30qdYShrqrq3m7nnEAF+fPxLI4E7fbwC856dTnQh1abWXq/F+IHzaQ+YsX08BHj1PTl3JBp90kxq9mlizjtQavCmzi8I65SeRwm/wfPVjPLmw0ZFC+VH6CkN04gx950V7GtnrGhThshFdXMWzXej6sZ8yxprHmpJVozrxBqqb9d6htTtr6YVc6TdQ+XlvpwjPiSR6Tk2jJXq4TXxKx9RA/v3UXb3Y/j9ktB9JjQjtWlFaSM7w78+a38c5Z23+mITKHdeOXvBJu3Z7pdWzbWVnNsFWBD+x5uW9zccxF9Fq2kB5DNvGc4QkGuRfy/eLH/QfGt4eCnV5n4NwRPcjM/IDdezQH2xG9F6B71RPB9MThH2T1UeMEDNDEUsolV5wGoyYd9Xw47aA3Bs3fUL1tG/vP00x1zd5+m4gRw+HJ6KM6jcsh2PV9ChLITIxhR0os/c+9jIgNIaQXbmJnmWZOvPOjryma/j6F07R0/hGnnUbKa6/gki6Kqosori6mY1xHv7lz9pTww0v+t6faES3N2kZw9p3dQGeERS/imvccb2U8gLN6KWDCHH2rV+Xb6/TmrPtdu4H3GdeC/me1orqigrdvmECr0w8QZTmNERe+5H9tLisLFmrvbefOrzF/6iKyd24nACHpcYP2GctemYC1MARbqQm7px7VvTNm4XY7mL+gAwDDh21Dr/9nE4gdrDiIDh1xljj0Qo9ep2dH4Q6Ky7czoPk5CCFwOsvR68O9r6GUkurqLNxuJ2FhvvTalZV7WbHy6Fz2EhJOIznpbCSSLVtu87br9eGEhKTQrNmVJCacRmXlHqKi+gAupHSh12v5d9xuGwsWdvaf1A0h35mpnmAj9h0DjmaS8jNdOOMvpX/aOZjNSVgszTgcsk4YcFVVBstXaGbWJk0uBiTZ2TP8jmkTfzfNu92G22ol77XXCGnbFmGxED5sGOUHC/nxi59IHe5fXuVYYitLxhyZ69d2aN3FICRJPWfUc1QgFbmdKNoxhrThrx6ztbnsoehNvoSOpemnULJ3KG5HMraycHTGKnRGK257KNFtFpDY7Ycjmn/DhjGUlyUCEr00YBBuLDEHiY7KJSY2mwMHOhHqDiFMmCjYNQK7zk5Kl5+Jb7nqsHMfWHIL0m2gtCyOKmMpOXFWomzlGKUTKXVIqSfUWUbTfTnsbteeMGsZnXZuJaykCukWbOnehdZyK3kdYtCtDqXNzv0kNE3EFllAeXI+Vae6iHlfjyFbh+EQ3qCFGppNewfzwIGs+u471u7fT7HDwZABAxg9duy/yidmLPAcMBlYC/iFAUgpi4Id92+jRoiRLgOrfz2Vfmf9BsBOOjBFPM1D8klasZcbxKdEb8ph5jv38GqPC/mzRX/Snxvvnaf2L4IpPMVO0emw594yqAtdlvoccXNH9GBjeVXQ9PLpQ7thfzyZ9/b0o/uN2/mD8QxmIWcvSofo5loGzVpcNOInqkPDubvqGlwu3xcxLm44hYULaB9zKc16PhV0XVsLttImpg3zs+bTJ6kP8ZbAonD5mel8er/vJn5bu2WYT38C7JXQajg0b7g4oB+ZK7W06l0vhPPfBym1FOmdzgZzBJRkUfTR+xx69xsA0qa+SP7jd5Lct4SQ6DpJxa74ERxWaNoLXtZ+Jcp24yl1DCbn2Te9w/7s3AJnkAKQ426/jw6DhrGjY/D3764b9GTHaV/Yx095nCFNh5BVnsXu4t2c2/ZcyrIcLP1+Nzl76hcQdQaB2+n7jnUd3oz41HDsrp0UOq+nNKMfJXtGYAgtpO/ZBlq3uw6TKRYp3cyb75+4sE/v74mK6oGU0k+QrsFRacYY5vPRKN4dSUzb4DZ3e6WB3T+2oOepl2Ju+QvllesBiI7qh7U6iz59vmPp0kEAJCefQ4vmt7Jm7fl07vQK8fFHV4Xk2j+uJaMsgzkXzPE+gNNL0/lwy4d0T+jO+e3O55e9v/DIkkcCjn26SRVhQSLEo6L6UFpafwXwYJhNSdjsvjDfhITTyc///cgu5mhwQsqdxoCHQA27msDoP9YQZtS0u3Mz53LX/LsCxlkMFl4c+iJDmw31vo72AwcodpUTYY7i4K23kdspDctPc7FIB7b2EvMWgRQ69Dod0uWqV5fkNOsovtCMbmcEcm8CcUVboUUUUS/eQnFFLuk7wqkoMBCbaqEou4SiPe1Y0/5TTuuwkgSj/7Pku8WXMrDrXzSJrk+/0XjcDjPlB3uSu+ZKpNvfx0hnqiSuw2/EdfjDr70ssy/SZaCqoC0pfT/FZbcghJuc1VdRXZJK85HPYwg5ev+aqvw2HFhyO26H5isSmrSN5F5fYIrIO8yRR0dUZB8SE8/GEDOcTdlbyXrXhNPp4vI7WuHavIHcJ55Aeny0ausL7QZtvzIEYoNd7rABNH1sEnaznn3719G379kB2njHoUO4q6qwrl1L+KhR6CMjcZeXo4uKYtqmaczeN5v0snRiQ2Ipqi6ib3JfPjr9o3+VEFPbcb72AQKQUsr/RAGNGiHG5dKzbOnFDBmqVSnYQ1smi+e4Q75IK+tB7gp9jfj1WXw7/QFe7HUJ89J61yvEfBf3DTOLDn/5P/Row3kbfPrXDWlh7IhJ4uKNvkrVLTJ30X37Gt597gWP9kPS40afpmZo6VAMG3+gOkSHSycIr/I4KE74lLm59zR4/rrh3wB7ivdw7s/nevc7x3XmwzEfEmoMdOCqKivlnYmXeffv6bDYp0i55vfGCTKOani6/ogIrvsLPjgVgO1fNwno7nhxtpaR9qZFEJaoVfQFbLt3s+/Ms0h55hlyHvF/AJZaTCxtF2j66p6cRtjwrnTqN5bsUWMB0I0fhftX/6reFz2kD0gmB9AkrAl/XKDdNFev3M6qj7Qw4kPh6Vx8/hjmf7IDoXOS1Ptz8jedj3QZcTstbDnne66JzaK84uiiaDp3epWt2+727ut0Ibjd1Q0codGxwwts39E4s8Xh6NL5dcLDOxASktqg1kZKyY97fiS9TBNUauid1Jupo6Yy4Mv+tDK7GRjmZFapkRKX78YpkFwUY2dA+N/POZOYMBa3dNA8bSLR0Q3fUx2OMmy2Qxw69DPpGT6jwdIKAz+XGJHApbF2eoQ2bl2mPYLojwwYigVrWws+PE3H1HcaPjY/Eu6+Qc9dP7rps0e73b4zTsf8bsJPe2m2S9plm7l4npnmBaXsT4hmb1Ljs1CnFJfTMbsQs1MTaiy9emHPyiTmoouJvfoqQKAPD0NKSYmthHBTOF9s+4L1eeuZlxU8S3WLyBZYDBZ6Jvbkyx1aglCz3sRnA68hzJhEdMQAwiLi0OksFBYtZONGLfFbnz4/EBXZ3ZMgswSDIZLls2ex6S8XfcZ3J7yrkwv/uEA7iecJHVvZhNN2XUNUdTwzejxLiSWPSJ2bcrcAt6BlThhtDoTRpNBnWpHxoUycPJXM/ZUs+mgfLts2HNbfSO5dQEV2KJWHLES3Kie6VRkRTSvRGQOfj9HRA2jXbhKV+Sks+GIn4WmCksoysqwZRB9ojrtKgM5JxwGpuF2SLmN0bNl5IUIGlu4o2R9BaYWepI6lCAlZdh1twrTPR4ldx9I5Tcg3uziQaKUo0o5b0Hhr5nHIVmwxWLA6G5f8bsvVW/5VQsywhvqllMFzWf/LqC3E7No5iI6dtGRpG+jFi0LzL3mq6nEmhU7h/J9+5rbfv+K5PpexqV0/1j/uU0mvW38FxcVaJEuXQTvovuzIH0hDVv7J4v7+au7W6ds573df+aeoFuW0HFN/Yq9RiwoAcFz8CYuy723wfKNG7sXlqsJuL8Zi0TJWdv0keDG+zVcF98moHYZ9Y5sVhBvrpEk6nMnqw9MhM3jIc12kG3Z84y/IJPcpIfLet9H3PJeD992Pq7SUpi+/xK5+/YPO4dAJ/uqqmRYGXnw5adkFfL3od9IKSulyUHvtVrYT9N8lefUcHcs7ag/RxGLJW9O0G8lfPQQLh0SzK9yXUfZ/nzhp50mAnDTpMYY5nvXeLITHU+bl1OBfdJcE/RHcV3r2+IyYmFNYu/ZCSsvW+/UNG7oBt9uB1ZpOds73ZGd/jcEQjdNZ4h3TJGUCHTtqGYwPHvyK0NCWFBYuJCNzut9c+35vRniTKhK7+StVw8z9qbStrHd97ds/RailBZGR3ams3IUQeg4c/ILYmEHM2v8nr+/Sbg3Dwx2cE1M3rZY/q1Yk0NQtEO0raBIXeMMfdMoy0ssP8syCy8iw64jQS+5O0n55PpcbQq5Dx9SRb5BsXYLFkkZ2zje07/ACmdUu3tzwJmNbjuXs1mejr1O00Oq08tOen5iXOY/lOfV/PlcO/J6MM8/27q8e2YRm8a1YkbWUs9e6cbSQ6IsF9lZuQtbp0Dm0N7rtmlUYwrVUAaW2UiJNkTgOZmNqpn0PpZS4KivZ3af+hHhWo56dyXFkxx6/lFyloQ4OJlpZ3aGYd1OeICu2ks/nTmXYBi3J5paWpexoXk6lxUVSkZnyUCdVZhcT2l3IowMnebVCGZs2sHXRXMbcdCc6nY5961fz4wuBmmCd3sCFk/5H4YEs5rw/FYDeZ5xLr7FnUpp3iIjYeL6Z8gjlhYfX5MQ2aUavM85h//o1lBcWkLevHoedoybQF2pO7zwcBjdunaRdVgTpyZXkxFUTVm2geUpbrmhxMdlrN3BgyUoirMZa8xAw17Hi50HZlIU5cRokQoJww4Wp53DXsAcpzc1h7i+fM52fcQuIb5JKTtEBwssFBVH2v7WkQU0GcXfvuzlQfoBmEc3YVriN89qd9+8RYv6/UFuI0et9v4ZW04/XhL/D3EMfv82YlYv5X98rWdq0m58mpqBgHhs3TSQ2dgg9e3zMlD3ZvJ3VsAoxxWwkx9bwTfyK798mOd9XHiC+SxHNBtWf3bLz9nK2dgy8qQ08ZQHLlg/3aztlwByWrxgNwPBhW9DrLfUKMa8Of5XRzUcH7Zv9xvNsX6plaW3TpT1dSn6gdUStB9/jRfVXtn0iyrPA22GZx9zTchjsr18Gzl4RTWm6v2bI3K4dtl1Bqjx7eONMHUs6C67+rYW3rf2zN/HGujfIqcjmm+cCfwlPeEjv96vlnIjBXPrIgoBx8W+9SsFtPk2I1EmKekgOXe3CGnMOKeUz611Xbd7NN7O9Wo8eibYa7dw/jZ1KxrYbcbvtjBi+HZ3OF45aowE0GCIYeMpCjMYovzmldCNE43N6lJZuYM/KrQghmP/+l/WOu+Spl2jSTvOZ2bzldvLyZjf6HH+X1i0mYzF2Y8YTD+Ko0B4E93z9C59t+4wX17xY73FtY9qyuzgwEd+4luM4r+15ZJZnMmX5lID+pgWS0lBN9f7IDDfd04/+/hh62miav6F9zu3VVkwhFtxuF0L4154qK8hn++L5JLVqg6Mgn5+nv1nflIfliuffILFFK6SULDywkANlBzi77dkUWAtYlr2MTfmbmL1/NtHlRs7Y1QbDoUBh8WhpN2AwmVs2Ul1RfvjBJ4j45i1IGzGInBaSuJA4pn0xmcGb/U3mPw/Opiiy1r3Z85YnFpsxOXVYbHrsBjcj1ifwd+g8fhy9h49jtXUz+yszSI1Mo6OuBa78Mn595TnCY+OoKPKPnWnbfyCVJSVk76zPlfb4MOCCS+g2+nSsOjt6u6SgKp9WTTvy3dRnqMjKoSwreAqGmCbNGH3dLditVbTtd4oSYo419Qkx6+jNy8LfDPHYB28yas0ynjnlLBYnDfUTYqR0k3XgE5qkTMBgCOO6Lfv5NV/TQtwg32IY83k6+1O2NW18Aa92pXlMKctg6/IZpA3LYc8vzel2nVaFt0mTS8nODnzIWCxpWK2+FD1t2z5GWuo1AKxZO4HS0rVBzxUfP4ouXd6hzxd9uKTDJbSIbMFTKwJ/Ka2/Yj0GnQGr9SBV1v3ExQ6moriId2+60m/cXR2WoBeez1Hvq6HnlZDYEUyhcGCNf86a0Hi4azM8kwLhyXCfdo24XfDNlRASDedM9Qk8gM3cmX2fFDf4+jWd/i573nmZqS33sKq9juY5od6bzsyhBykN9/nT6F2S1Hx44SPtM1B97ih6PvsWX+34igVZC5g0YBLNIpr5JeILhrW7m+Ib6y/+Z9wriPpGT8HDTiwrdTiau3Emw59lBhKaTuSTbQ2EbQBvjHiD1YdWc3evuzHqtQd4XYfOY8XWhXMJjYqmZY/ebFs0z1uGojbxA7rxUuwvPD3oSTrHdyF93dgjOkdUZE+aNrmazPXp7N7yGYc2xGspb4GOl+zBXOtBcmBJEgVbYzjcz8OLnniOyNQm5DmKmLVvFh9s+cCvX7i1KaSA5IIQKi1OysP837Mwq6RVrmTS14dPN5fVswuHTDoSY+JJ/V0zqVQb9bR+7nmixo+nwl6BxWBBr9Njq6rio7tvpLIk+Gf3iuff4LMH7zjsOWtz5nW3EpOUzPYNaynOz2X4lROJSmzARNsIcvbsZPmcmdhdDg4uCtS6Dbn/TkxFDpZ+8znV5UeX1+SiJ5+nWYfOSLf2Gu9etYyDO7ax7refiWnSjLG33E1p/iF+ff0Fv+O6jDiNUy64hMj4BLK2bSY/I53QqCha9+nPL688S/dTx+Kormbn8iWkb1xHYotWFGSlc9O7n2E0hwRbCgBut4vK8lJ+z5nLlJWaMDuh3QR2Fu8kNiSWpwY9RaQpUnMidzu5Z8E9zM+aD0BcSBxjdrXAtKXAO1/TDp04uCNQ0DgUU81Nd71My/bdjvn31uV0UlFUyLbF82jbbyDpG9ex8LMPDn/gCeK+b35VQsyxpj4hZjPdeE5M9hs7efrrDF+/gtfP0vG77gU/IaYuzRZsoMZ382N5EUac7F18L48PHRgwNq4oj8LYRL+28IpSZjQNp2f3Hvw16xQMEf7SeFb8vezb/zrDIhqullvb78XhKCMr60MkkvT0t/zPF96RigotkuVA4mNc1UUTfJxuJz0/05L6GYXEJeGP8e+wddPVAPTr+zMREZ15/crzcdZK8gUwsc1KIo11wnTv2Q6v1BEEzv9Aq9B6OHK3wDTNsZRTboMxT1M+bx4HbrnVO2R7M+h4AN48U0ezCy7jqx1fYbLruHB+U4we/4qr35rO8NljvMf0SOjBxvyNrLl8DbqySvRRUQFObC5XFS6XFXmglH3jAt/3qv4uSq6q368hYqae0NU69CVBfGleeJ6IMWOw7dzJrl+/ompQN27c9zTxlgQqSvNxC7CZ/I+LrJQkFUO34efzQL8HcUonkaajD96XbjdVq1Zhat4cfVwcFVQz5rsxVDh8Xn+P9H+EgU0G8tGUu4nICgy/LoiyMa9XPlXeolLSkzZdcAejsGTqaNauLa6Elzi0IY6cVQmMu/0BZr8RqD3pVGolsdJGaH4h5rZtqNqzl01pieRE+2d4vvXDr5l67cVBr6mZQ9KtWSsihg5krn4XP9pWMyGnN9krggvyNfTdm01ChdVzBZrI5AY2pSVSZTJy+m33EC51LPxpBruL6zdrpHXtwXkPTUZv0IRNt8vFq5eeXe/4hug59kxGXn0jVaUlWCKjcNpsGEPqfyD/Uzhs1V5Bobqigt+mvozDZuP8R6agNxw+VcV/EZfbFWCO/C8g3e7Dps6QbjcSSUFmBj+//DSleZoVoK6G6Mx7Hia5VVsiExK986ZvWo+9qhKXw8HSb7+g9FDu/08hRggRC3wAnAYUAA9LKQNUDEKIqz3jajsWnCGlXHAk89SlPiHGgYGrhX+I35RprzFk40reOkNH1zN/5qZh9ecoaL1oE5Uu7RdGTSZfgFV/fsDpj/Rj7Fqf6aPf+kWs6umfRv7+aY9x/sNP0qJH76C5EP7Qn89v6b8xqf8jxOkcNHHvJDv7a78xzZvfRJvWgSUL8goXsXnjNfWuvTDxfiZ0ucm7//yq5/li+2e8Wo9Px7ChGynLK+Wju2/0a2/XfyBnltVJHNLjMi3yqIYWQ+CSr7QopMZQnAEr3oZTnwKDZlaRUvLdj8/ySu6XCKBNtmR9G+3Lef78pkRY/W+e986Yhc1l47TvTqNFZAs+Pv3jw/4iWr3mPMrKtKrbPXt8inGTA31UFKE9e7JxzfUUlM33jo2PH43hyoW4w6FoopP41wzorIKmr7/OwTvvpMWMr8l++BGSHn2ErOsOn98FYOIdehJK4EA8XDnPzegN2nfUqYNLH/Rd35wL5pAU1vCvcCklOBwIk+/1CxaNtb0ZTL4i+IMnttTEWUtTgs+PG1ErX06oy03LnEK2NmtY9d6m7wAi4hJIfOF19A3cgyTg1Okwen7Bx99xO9lTp5IbFUZmfBRllhMfDp7Uqi2H9jVcM6qG8Ng4rnl1GqYQi1b3qdqKyRLKd09PImPTeoZcejV9zzr/uGjXFIp/EiHECRFiTrS4PBWwA0lAD+BXIcRGKWUwz9jlUsrBx2Cew6In8Fe13lNvJj1J0MHZsJr5gy4t/KKMaogqt9Iz0t+f45R1C/yEmJRDWv2Z2KapuFyBdQfatH6AD7dqDshPrdTyv3xx+idQS4ip6ztRmwUFubyRHcKgcCc7rHruSPI/Ry+Lf8zdPX3u4YzEFHL3BU+9lJM7k9RmV3DL+1/itNuZfsvVAOxauQzboM6Yi3xvwY6KH0mONBB92nTocn7Q+RokpjmM9ReMhBBMKfsKQrWb/vo22n+9Cz8Bpl3/QQy7UhMazHozCy9qnO+5w1HsFWAA1m+4kvbtpmAwlLJ8nr8GqVWre2jZ4lbkagf2jAyq1q4jZv0Eb3/kDk3b1Xr2r429YgDeeyO4lsfghuc+cvLQNdp1jv5O81ua2HUi721+j4ldJ3JHL595orbA0vzzzwjt0wfr+g1B5+54AL551okrMZYdIUVMP11PZBUkF0uWdrKxof1+Xrrrd+yfz2TdD1+zJ0krpyHqJPyr0uuCCjApxeXkxESQUlxOz8w82OgfLaePiiJizBismzYRe8XlRIwejaukhNyn/kflEl+174I33sQEpBWVk1ak+V5UR4Qzr1VwYa5nei5FYSHEV1hJKqvCJQQ6Kdl56hD25WUHPaaG6OQUSnJ9xSuvfuUdYlKaoPP8Es/ZvZOopGQqigqDmoWik1K49vXpXgFFCIHJot0PLng0eMoDhUJxZByVECOEsKDVTtotpaw/zaL/MWFohSO7SCkrgCVCiJ+BK4CHGjz4GM8jhP8vP12QTJUGl9aWmSgoq27YIXd4rKbaHxMfSXP9q2Tkao6fsU1tuFxVnJcUww+Hihm/4Af6Dvc5zN4/7TEAUjt1JTIhEas1MBIpIeE0NuX7m4OK7T7HuffLWjIqiABTbi/n213f8uraVwEds0tNrLtiHQ/82JUzon3Xk5HxDqmpV7NkSfAIH4CHD1h4tpmmmdlVuJXUZmCJ0K553B33e00Eby2N5d4JF+Ic/RilH/XhYBMLB5tYGN52NI1VwO4o2sGFv1zIN2d8E5BgDqDA6rNDX9vlWm7reRtvzPoffOEzG5xywaUMvPDSRp4RCgsXs2Hj1QwetJxVq88M6N+5yz/JYEryeXTq5DOLCKMRc5s2mNsE5m6pTbs1q9l//vlYuncnavx4wgYNomrNGkq+/4GYiybgKivnwC23BBwXOW4cCffczd7Rp9IqF95yX8xtOp8Q+95mLcNwxZvT2L78He2Ya67Ele7zl8q4/AribphI4XRt7JX36DG64IPX/QUmfV4RnYHXp/vab/XIYEXfaNF07YB2ucVUmoysaZmM2emiKNxCSkmF1wTUY/AIkt7+gIIIC4llVQjQhJdgr8uK5eijowPa9VFRpL2vrVc6HOS99hpFH3xI0mOPEXv5ZX5je6IJbQ5bNXq3JOvmW7Cu1Pw7Ukp9aa30UpI8+XE6XaIVdi3KPsCaWTPJ27+PM+9+kLCYOAAMRl8uEru1CmOIJUBbktJWy00UGhnFvTNmIaUkc/NG8jL24bLbGXB+cNOXQqE4djQ2xPpjYJWU8m0hhAkt4V1nNG3IuVLK3xoxR09gmZTSUqvtPmCYlPLMOmOvRtO2WIEi4DPgWU8JhEbPUxdjaIjs1C642nY7/pk1Wx84QERVGVvTBEmmzqTF1l/8CsDtCcnP3lVMaKJ/5ExYeFeKrdXYsw5isoTiapKGNT8LU5nv5praqStOZwXlFVupckGoHmyYSYzqxtpDgXZ9ARgEOCT0TQ4MzVydu9pvv2ZMub2cvcU7MOskyZ4cCAKBDCLIWUKaUu6oJr1Sc0xsaXZT6RbkOQRtYtogpSTCFEFVfiGV5bmAoDRakGzwN0WZTAmEhbYKmD8Yq3NXE1VpxGLTY7CEkNKybUA/QMfYjoSbwqkoLqI4x+cp36xjlyNWzReXBDozxkT3R0oHJaX+WXktIamEhATmsDlWSLsd285dhHTuRNWGDQijEUtXLYpMOhxY12th1ua+vcksy6TAWoDBBa1zJYZ63HREeDiywqdxqzbC3hTtNar92XFXVlLdiCrQIZ07owtrhNO6260VBjSbQUqsm7cgbdWEdOmKff8+kFqkmTiCAokKheK/wcKFC/9V5qQxQE254rOACCAZuBZ4AjisEAOEA3UTiZR65qrLIqALkIEmLM0AnMCzRzgPQogbgBsADEdkP5feG72uEQ9FnYDSPCvBoikqKzZjAtwhZuzWKlKNArcjh4N1Kis5XVrSskKXjkIXuHETXU9NG4kmwICW58JisOCWboQQlNhK6l1nhCmCHknag6vm4R1MgDGbEggJaUZICCREgM1lo6p8A5F6SZ5DsKfYl4chwRxJVLxmpqqs1gd8quz2fIQ+AospGiGMpJelk1+VT/PI5sSGxKKTWs6IcmsZMeUmzA7NROG0VlNZVcbBqhxCKwRO6QSP6BpmCsNutfoJMOExsUcswFRXB5oUwkI1jYoQRiLCO+GWDhz2QiyWNHS64+uDIUwmQrpq5QRCe/Xy7zMa0UdF4SotxbkvneSyMpIc/u+dy6DDpncT6rEa5kUL8iMr6eyRYQoiBXnRmmmjZ6J/ZXZdWBih/foFLkpKcLu1yKgjcdjU6dDVOKQKgaV7N29XSOfO9RykUCgUjaexd6QY8NY8PB34XkqZJ4T4GjhMFUIvFRBQDzESCEgqIKWs7WCyWQgxBbgfTYhp9DyeuaYD0wFi26fKV14J7jtymXjfb3/Sqy/z7PkbALih2efcPqptkKNqncctefsWzeEzquUzpPTdHzAmd10cTRNvYHjiXtzLN/PqDi3L7fDrLqDtgF6sXnMu0IQnskO82Uv7pvalMqsyYK7atIlvwxfjv6DrJ10Z13IcWwq24C73+fGkRqQy+7zA3B7p6dPYu89nFunc6VVyD/1EYeECOnZ4jiZNLvQbX+N0vKpSz5dF2sNcID1OwIGaqp8KTJwdXyOEVWMrLeSnVU0p7BBBhIzgurnNMFic2EprBINwaBFMyxHY1qJHb3L37KK6heZ/ce+MWfW8Ov5omkc3a9ZcQFn5Ju/8oaGtqKraR/du7x91Wv0Tgbuqip29ems7Yf7RO2mffkJYv37kVGh+HD/t/YmpG6YSATidkqhKePyM5zij1RkoFArF8eREOas3VojJBboIIXLQtDI3eNrDgYYdRnzsAgxCiLZSyhrX/u5AY5xxa6dL/DvzNBpRy8xWLUtYd2gdvZJ61TveXu0Lfz603kxKkOSbpjAnPU8/ExY/hs5zNULvpsTwFKtrlX+pcHlyZ8R2ZEHWAgA+H/c5neI60euzwDVsKthEhV37qT17v7+w8tnYz2gf2z7omhMTx3iFmOHDtqLXh5CcfBZVVfuxWFoEjO/dawZr111EvzAXX3ry201OCZ7yfv4XrWleYSKjbQnNR2oPVXOUg3MHZVGUbWHI8mQ6X6OZ3bZ80hZn9ZG5Z6Vv8JnYJk79yLstpQurNZPQ0JZ+48vKNnmExECiInvSu/e3/4kIEV1oKIn330/ei9r71vzzzzTzjsWXWj0lXIskuqn7TdzUXYs8yyrPosBaEKB9USgUiv8yjX1yfIhm0skGXEBNgZn+QGAZ5iBIKSuFED8AU4QQ16NFFZ0NBCRT8RScXCelPCSE6ABMAr490nmOFTMPPcCnBwu86fi/fmolhQcrufntEeg80khxrpb50u0qBik4uCKRpgP8HRlj25diMJkgrT9s/oYzmm5nvjst4HwTOlxGYXUhO4t2YtKZsLvtdE/oDsCzQ56l1FbKxe0vpqi6iJHfapVrb5xzY8A8iy9aTHRIdL3XFRrakkEDl2A2J/s9wOsKADXUrjuzasJsdu17jYJDPwIwLd/MTQmaDcNebiSmQtN4Fe+JIjzFSlzHEgCMoS5ualOBNSbLO1eXqzRZNHdtHN/adXx5+x+ERkbxysQLkWVW2p8yhB0ZGxHZgYm2+px5HpHxmjamdmXnpk0vpUN7LQKkdsh0XUymePr0+a7e1+jfSNx11xI+cgT66GgMMY2rlZMakUpqRGANKYVCofgv0yghRko5RQixFUgDvpVS1tgInMDz9R8ZwC1oAlEeUAjcLKXcKoRIA7YBnaSUmcAo4GMhRDhwCPgceOZw8xzBOvzIyWkTYLEoie0OaA++cqcWEVOT6KjwoGbeKTxYQUKq5orz3fPLQDpxWrWaSi5b8MRCxlCJtfogFqDdPV9zYFdgBMPD/R/m+j+vJ70sHYBLO/gibWqbAhJCfaGsm/I3UZspA6c0KMDUEBISPP9H/eObUl190K+sgdmczPSzZ+GyVjH77QvI3xLr7WvbbxC7Fy2jaFcUbc/2BbJZ4gLDyZN7FzK9/yJCw7Rsvfe896237wygrCSDP96fQrczWtGm7QPo9T6HUCldXgEG4ODBLwkPa095+dYAAcZojKV7t+mEWFIxGo5fLZrjibllcEFToVAoTiYarcOXUn4fpK3hvOmB44uAc4K0Z6KZpmr27wPuO9J5joZVK8/DZgsNIsT0AT71a6t2VZO+tMS7b6usYPuStXQcPBxb6Ucgq9AZtF+7ITHBHXKXLR+By1VBaqswzK4NAf0vFTZlFLC1wCeT1RZW6vJQv4d4btVzAe0jUo+PX0eXzq+zZq1/vpRePb8kNCQGQmJo1fpmUhLL2DTndwDOvOsh3rx6ApW5UF3Zi2hTEtVGnx94xw7PsX2HLzLeXrwAwvzDZx2OMjZvuYXi4uXE9oID2Qs4kK1VRe7a9W0SE8aQm/tjwFp37vJlYU5MHE+H9k8F1BtSKBQKxX+XeoUYIcSV9fXVRUr56eFH/Tux2UIJFlEkgkSeZ5RlsOCLXO/+kq/f5uD29YREJIOsMSdpziLSFdy/wuXSfFeymlmILvSVs99q1fFegRmjTut/bcRrXP+nlqxtQ96Getd/cfuL/YSYVZetoqi6qFFamKMhKqonoaFtqKrSopMGD1qG2exLNDb4Yu1j0+O08ZTmHULodNw8/XO2LZ5Ht1GngxAsmN8LN2UMGriYkJAm6HRmrNYM9u1/jZ27HqdJkwvR6Uw4HKWsXn0O1urMoGsB2Lz5Flq2uIP0DC0/Sru2k0hNvZoFC7vicmnvSUrKBXTqeCQKQ4VCoVD8F2hIEzO1zr4JMKKVFgHQoTn12qirsvhPEVzY0AdJ0jtj5wwSGebdP7hdy9mxbfF2b1tKm2bk7N7JmEtmsGFH8ErQNbjdPp/o9wq0UFSHp622E/GF7fyjhPzWWauOR43PTtPwpg2e9+/Sr+9MFizUcpeYTIlBxyQ0b0lCc83kYQwJofup47x9A075idLSDd58K8nJZwGwb/9rgOaEGxnZjW3b7w8qwNREEtWwP/0N73Zq6tUADB+2Gas1i8rK3cTFDT+6C1UoFArFv5p6hRgppddZQAgxHi0fzF1ATWaw/sArwP+7/NntD9gJtQeqYoy62j4YPgHEYfXlGsnZrVVljk1pATugZH846X+m0mxwHvGd/Qs7lpVpQtAHBYFh30adkWcGP8O8zHkMSx0W0F+blZeuxKA7cRUk9PpQRgzfiZT2o4rosVjSsFgCHZr79Z3FqtVnsHbdRZjNydhsPq1Xl85vEB3dx0/rU16+lVWrz/Lut2p5d53zpGKxKGdWhUKh+P9KY598LwHXSimX12pbKoS4C/gYaFySjv8IE5ZWBLS1z+tHn34D2AtIdyW20ne9fXvXBOb6E0IwePBKpr6nmVeatetDNX8EPd/mWjV/Tm9xunf7zNZncmbrBpMQAxBqbDib8PFApzNwrEtvhYe3827XCDAR4Z3p1+/noOMjIjrTp/f3RER0xuEo8hNwFAqFQvH/n4Zrc/toAQTLuFaFFrH0n6ddrs9sUVOK4fQdE9G7jJy281pG7L2Mva9rL5fb0ahyUZhN8Yy/42EAeg19wtvee0NJwNjWUa1Zf8V6nh968vpuCKGnVy9fTSCzObleAaaGqKge6HRGJcAoFArFSUhjhZiVwBtCCK+zhWf7VWDF8VjYiWbkTl+NHH2llnmuRXEXoqsTaFXU3W+so+r3BudK69rDu93+lCHcO2MWFovPdySqzBlwzMi0kRh0BnSisW/J/09iovsSH6flvunV84t/eDUKhUKh+DfT2CfmdUAckC6ESBdCpAPpQCIw8fgs7cRjcDnpu2Ex0h2YVK2Ghgpm1kTm2CobLhNQ24vkmyLNz2ZlbmARwpOV7t3fY9TIvYSGtvinl6JQKBSKfzGNTXa3VwjRDTgV6ID2HN4GzJGNKYP9H+H6JbOI2L4GoevqLYk4fO+lfmPczvrDfdM3atqcQ/t2B+03E4a0l0NovLdtWaUmxAxuOvhvrFyhUCgUipOPI0l2J4E/PX//v5EupJTYSl4lohiIucfb1ZA/TL+zL+DA9i2ERNStT6kxcJHn2NajqMkGPGnAJFpHt6ZHQo9jtHiFQqFQKE4OGi3ECCFi0SpYp6HljPEipZxyjNf1jyKkC1vJq0H7XDZfpUadsTVux14AzNG30aRzF6wtwkg8LYhWxV7ltd0V6QSPHrRgFpJ70sz0Tup9rC9BoVAoFIr/9zTKJ0YIMQDYjRZq/RRwLfAoWmmACxo49D9JJTsbNc4UfrZ3WwgTH27/iBmdtvHCAS17rM1lY8x3Y1h0YBH7PxlD15ZpdG2Zxp7k9lS6BUUuHac2P/W4XINCoVAoFP/faaxj74vAF0BToBoYiaaRWcORFYD8T1M7wV1dfms/nf2l+/3avtr+FdmV2dw691bOMpV426878AsABmH4R3K8KBQKhULx/4HGCjHdgLc8fjEuwCylPAQ8iJbJ9/8Femv9UUVS2iCIEGOOuhFz5PVkxG7F6faFTn+69VNeXvtyg+ebduq0o1+sQqFQKBQnOY0VYmqXZD4ENPdsVxBQ//m/S30J9N3ObGwlU3HZd3jblntKCAhdGEKvOfL+lfGXt//FNS8e9nx9k/se/WIVCoVCoTjJaawQsw6oeeIuAP4nhLgKeAPY1NiTCSFihRAzhRCVQogMIcSljThmnhBCCiEMtdpaCCFmCyGKhRC5Qoi3avc3ltIS/+KFoeHhQcc5rVq1hRonXoCdzX2lCX7s/HrQ4w7nsHuyJ7ZTKBQKheLv0Nin6KNATZXDx4B84E0gBrjhCM43FU2rkwRcBrwjhOhc32AhxGUEj6B6G8gDUoAewDDgliNYBwCbNo3x249PbR50nNuphUZLd5Vf+wf9HmBhq6/JjdgX7DC2F24P2q5QKBQKheLv0yghRkq5Rko537OdL6UcK6WMlFL2kVJubswcQogw4HxgkpSyQkq5BPgZuKKe8VHAZOCBIN0tgW+klNVSylzgd6BeYaixGIzGBvulWzMhZSRrvi8OvY3tScvrtUNVOf2FnvcSR3q3nxz45N9YqUKhUCgUiiOyZwgh+gghLvIIJAghwo7AjNMOcEkpd9Vq20j9wsczwDtAbpC+14GLhRChnhpOY9EEmb+F3tC4S6kI8RdOvhz3pd9+b2t10OMGHNzm3T63zblHuDqFQqFQKBS1aWyemCQhxEpgFfAlmjkI4BWg4RAcH+FAaZ22UiAiyPn6AIPQTFbBWIgm/JQBB9BCvX+sZ+03CCHWCCHWBOuvM/ZwQwDY083tt98sopl3+83cfNZaQrz7vaprCTQHVh3xuRQKhUKhUASnsZqYV9E0InFAbTXEt8BpjZyjAqibjz8SKK/dIITQofm83CmlDCj37On/A/gBCAPi0XxzguarkVJO95i9+hxuge3bt2/EZZh4YeRLfi1R5ijvdt9qfy1MtMvNAKuVPtZqeMA/j4xCoVAoFIqjp7FCzCjgUSllcZ32vWhJ7xrDLsAghGhbq607sLXOuEigDzBDCJELrPa0HxBCDAFigVS0vDU2KWUh8BEwrpHrqJfu3bsffpAwEKIP8WvSCR2Pn/I4X5/xNWFScm+h72XSA+9F9eWjidshNJaZZ81k1rmz/u5SFQqFQqE46WmsP4sF/1wxNSSgZfA9LFLKSiHED8AUIcT1aFFFZwMD6wwtxT/3TCqaGas3kC+ltAsh9gM3CyFeQjNTXUVNRcWjYOScuRg6dgho14f0x1W90q/NGGKmS3wXeif15o6ed9ArqRcAF7a70Ldgp0+BNGDUs9B+gne/TUybo12mQqFQKBSKWjRWE7MIuLrWvhRC6NEy9s49gvPdgiYQ5QFfATdLKbcKIdKEEBVCiDSpkVvzhxbODXBISlkjSJ2HVowyH9gDOIG7j2AdfoRXVNDUVkdGE4Jz7rs5YKxeb8SgM/Dx6R97BZi6tHBomX3D3W4/4UahUCgUCsWxo7GamAeAhUKIvoAZzZm3MxCF5oDbKKSURcA5Qdoz0TQqwY5Jp04Qs5RyAzC8sedtDMKg99tPatmaFl3jA8bp9A2EYUsJQGuHkyUlEDXkQVAOvAqFQqFQHBcamydmG9AVWAb8CYSgOfX2lFLubejY/wI6txv0mjx3+i2aQqcmemjA+Zf4jbVEWOqfyO6pvXTqFKLu3Ay9rjz2i1UoFAqFQgE0XhODx7Qz+Tiu5R/DbLcj9JomJjpZc8cROk2+63HaOFZ8/5V3bGhUA1Wn83d6NpT2RaFQKBSK402DQowQolGRRx5z0H8bjxAjpZYDRnjqGukN/uYjg8lU/xw/TNT+750Lg+449mtUKBQKhULh5XCamHRANtAvPP36Bsb8J6jRxODWLlfoNG1K3Sy+9Qoxv9wJRR7LmjHsuKxRoVAoFAqFj8MJMX1rbQu0TLmXomXJ/X9FjWOv9MpsmhBjMJvpOHg425csAKC6oiLw4OVvw9qPffvDHzp+C1UoFAqFQgEcRoiRUq6tvS+EcAObpZTByzb/l/E49gqvP4tHIyME426/zyvEZG3dFHjsoTr5+mJbHqdFKhQKhUKhqOGICkD+f6Z6s1aMOzw2DoDUTl0bf3DLob7tkCgwB5SDUigUCoVCcYxRQowHe3o6ANHJKVz3+nuccuGlfv19zjyv/oNn3uDbrq5b41KhUCgUCsXxoNEh1rVoyNH3/wXRySkBbSZLA/lhFAqFQqFQnHAOF2L9c52mEOA9IUTtStZIKc861gv7t1ETat2gRkahUCgUCsUJ43CamMI6+58fr4X824ltmgpATIqnNmX2BkjqDA6r/8Dmg0/swhQKhUKhOEk5XHTSNSdqIf8qVr8Pv94LD+yH0FgA2vTpzyVPvURK2/ZQsBumD4NTbgNzpP+xYXH/wIIVCoVCoTj5UI69wajJ+VLin4i4SbsOWk2lKo+CKmsVpHT3P7bH5cd/fQqFQqFQKI7KsfckwD9XTGC3J7uvdHnzy9B6JFwx87ivTKFQKBQKhcZJq4nJPti+/k5P3SRkPUKMy679P7gW7B4f51OnHLvFKRQKhUKhOCwnVIgRQsQKIWYKISqFEBlCiEsbccw8IYQUQhjqtF8shNjumWuvEGLIka3FX0CJOr9W1JFXiHEHP7i2M6/DI8QYG6hurVAoFAqF4phzos1JUwE7kAT0AH4VQmyUUm4NNlgIcRlB1iiEOBV4HrgIWAUEJnY5DDUizIi58wAI7dVba3A5IHudtu12Bj/YWUuIsZVr/02q6KNCoVAoFCeSE6aJEUKEAecDk6SUFVLKJcDPwBX1jI8CJgMPBOl+EpgipVwhpXRLKQ9KKQ8e0YKk5vcSE6dFE4UP85QOcFb7xrgcwY+trYmZfZ/2X2liFAqFQqE4oZxIc1I7wCWl3FWrbSPQuZ7xzwDvALm1G4UQeqAPkCCE2COEOCCEeEsIETSlrhDiBiHEGiHEmtrtNZqYpi88T9LDD2GIj/d01DIzuWzBV+aoCmxTmhiFQqFQKE4oJ1KICQfqFhYqBQKqJQoh+gCDgDeDzJMEGIELgCFoZqmewGPBTiqlnC6l7COl7OPfoWlijMnJxF51la+9tvbFWZ8QUx3YptMHH6tQKBQKheK4cCKFmAqgTmY4IoHy2g1CCB3wNnCnlDKYU0qNLedNKWWOlLIAeAUYdzSLEkL4Nyx91bddnxBzaPPRnEqhUCgUCsUx5EQ69u4CDEKItlLK3Z627kBdp95INHPRDI+AUaPiOCCEuFBKuVgIcYC/WYhSenLBeIWYj8/QfF0O1rI61RZiDm3TopYSO8D6k7b6gkKhUCgU/xpOmBAjpawUQvwATBFCXI9mBjobGFhnaCnQpNZ+KloEUm8g39P2EXC7EOJ3wAHcBcw6sgWJmnVp++mLA8fU+MRsnQnfXq1tX/uHrz++PRTsPKLTKhQKhUKhODac6BDrW4APgTy04pI3/1979x5eVXXmcfz7RgLhLl6gRLQRi6KAjTbM9IbYBxEsWqVMvSBeOjrBtloZ5XEqj3RAkGorraUqLeMtD1aqVqmttTVWoyiXQdpCKegoKCKIgNyEBFD0nT/WTtg5ObmSc06S8/s8z344Z619Wfs9wn7da+293H2VmR0HrAZOcff1xAbzmlle9HFzrHtpGnAU4e7OPuAx4LZmb+0H0Q2jN587WPbAiFi9EhgREZFMSWsS4+7bgQuSlK8nDPxNts06Ds4DUFn2MSEh+m6T29KQlRbfDSNuO/jyu9p0P7apzRAREZEmytppByq7kxqkXYe66y+YfWhtERERkUbL3iQmkpubW/9K7fLqru/Sq3kaIyIiIg2WtUlM5dNJeXl5ULG99hU/fA8Oa5+87urnoe/X4OgTU9BCERERqUu6B/a2TOVba69bdDd0qPE+Pjj2i9CnCC7/XcqaJSIirdfHH3/Mhg0b2LcvyQtS24i8vDz69OnTsF6NFMjeJCY+Jqa2iR4B3nkFTjm/Zvmomc3fJhERaTM2bNhA165dKSgoqPli1TbA3dm2bRsbNmzg+OOPz0gbsjaJ8fgDT59+UvuKm1ZUn/AxtgcREZHa7Nu3r80mMBBeFnvkkUeydWsdvRkplrVjYgDskyh5qetODMAHb9QsO/y45m+QiIi0KW01gamU6fPL3iTG4bCPon7KvXUM7K1NXvfmbY+IiIg0SvYmMfHupNIfZq4ZIiIiKVRQUMCgQYMoLCykqKgIgMcff5wBAwaQk5PDsmUH5wxcuHAhp556KoMHD2bNmjUA7Ny5kxEjRhycpqcFydokptpP8dGemiscP7T69w668yIiIq1TWVkZy5cvr0pYBg4cyJNPPskZZ5xRbb2ZM2fyxBNPMGPGDGbPDi9ynTZtGpMmTcp411EyWZvEVJM4sHfUT+GS3ySs5HDez9PWJBERaVvMrNZlzpw5VevNmTOnznWbw8knn8xJJ51Uozw3N5e9e/dSUVFBbm4ua9euZePGjQwdOjTJXjIva59OAsgt3xU+JL4HZvBVNROb/R/CF66EP1wPnx+blvaJiIgcKjPj7LPPxswYP348xcXFta578803U1xcTMeOHZk7dy4TJ05k2rRpaWxt42RtEpO7fQt5H0Y3ora+VnOFnMOSbzhpU/1zKYmIiCRo6JiS4uLiOhONxlq4cCH5+fls2bKF4cOH079//xrdSJUKCwtZsmQJAAsWLCA/Px9356KLLiI3N5eZM2fSq1fLmWona7uTzJ0m3ZRr36n2BEdERKSFyc/PB6Bnz56MHj2apUuX1ruNuzN9+nQmT57M1KlTmTp1KuPGjWPWrFmpbm6jpDWJMbMjzGy+mZWb2TtmVm+/jJm9YGZuZjXuGplZPzPbZ2YPN7QN27eHH9MdMIs+1OLs6Q3drYiISItTXl7O7t27qz6XlpYycODAercrKSlh1KhR9OjRg4qKCnJycsjJyaGioiLVTW6UdHcn3QN8BPQCCoE/mtkKd1+VbGUzu5S623gP8GpjGrBnzxEcccR70f6BHetqX3nQhVB6S2N2LyIi0mJs3ryZ0aNHA3DgwAHGjh3LyJEjmT9/Ptdddx1bt25l1KhRFBYW8uyzzwJQUVFBSUkJpaWlANxwww2MGTOG9u3bM2/evIydSzJpS2LMrDMwBhjo7nuAV8zs98BlwA+SrN8d+G/gcmBxkvqLgZ3AIuBzjW4PHm7C+Ke1r5Tb8eDnaxY29hAiIiIZ1bdvX1asWFGjfPTo0VXJTaJOnTpRVlZW9X3IkCGsXLkyZW08FOnsTjoR+MTd4+/wXwEMqGX9GcBs4P3ECjPrBtwK3FjfQc2s2MyWmdmy+tatoX3ng58/U//tNxEREUmfdCYxXYBdCWW7gK6JK5pZEfAV4Be17GsacL+7v1vfQd19jrsXuXsRwIe7egJQvqVT8g1OGHbwswbwioiItFjpHBOzB+iWUNYN2B0vMLMc4F7genc/kPhiHzMrBM4CTmtKI3bsOIbFiy6kw3uv0aFdwqDe770KPQqaslsRERFJs3QmMW8A7cysn7u/GZV9Hkgc1NsNKAIejRKYytshG8zsW8AXgAJgfVTfBTjMzE5x99Mb0pADBzqQR5Knko4+sTHnIyIiIhmUtiTG3cvN7EngVjO7mvB00vnAlxNW3QXkx74fCywlJC9bgb8C8TkBJhKSmu80rkGVf9YxsFdERERarHQ/Yv1d4AFgC7AN+I67rzKz44DVwCnuvp7YYF4zy4s+bnb3A4RHtCti9XuAfe6+tUktSpxeINGlT0D3Pk3atYiIiKROWl925+7b3f0Cd+/s7se5+yNR+Xp37xIlMInbrHN3ixKYZPuc4u7jmtAaHAevJ4npdxb07N/43YuIiLQABQUFDBo0iMLCQoqKigDYvn07w4cPp1+/fgwfPpwdO3YAYYqCU089lcGDB7NmzRoAdu7cyYgRIxo8bUI6Ze20AwBfGnNJ/XdiREREWrmysjKWL1/OsmXhbSO33347w4YN480332TYsGHcfvvtAMycOZMnnniCGTNmMHv2bACmTZvGpEmTmm0G7eaUtRNAXvfQ43To0AHe+3ummyIiIm3chAkTWL58ebPus7CwkLvuuqtJ2z711FO8+OKLAFxxxRWceeaZ3HHHHeTm5rJ3714qKirIzc1l7dq1bNy4kaFDhzZfw5tR1iYxVRnlp9HA3rOmZKwtIiIiqWJmnH322ZgZ48ePp7i4mM2bN9O7d28AevfuzZYtWwC4+eabKS4upmPHjsydO5eJEycybdq0TDa/TlmbxABh8sfKMTG9BmW2LSIi0mY19Y5Jc1i4cCH5+fls2bKF4cOH079/7eM8CwsLWbJkCQALFiwgPz8fd+eiiy4iNzeXmTNn0qtXr3Q1vV5ZOybGnp4AUw8/OCYmJ2tDISIibVh+fnhrSc+ePRk9ejRLly6lV69ebNq0CYBNmzbRs2fPatu4O9OnT2fy5MlMnTqVqVOnMm7cOGbNmpX29tcle6/c/4heNVN5JyYnu29KiYhI21NeXs7u3burPpeWljJw4EC+8Y1vUFJSAkBJSQnnn39+te1KSkoYNWoUPXr0oKKigpycHHJycqioqKhxjEzK2it31RjrT6Mnt03zJImISNuyefPmqtmqDxw4wNixYxk5ciSDBw/mwgsv5P777+e4447j8ccfr9qmoqKCkpISSktLAbjhhhsYM2YM7du3Z968eRk5j9pkbRJT9crequ4kJTEiItK29O3blxUrVtQoP/LII3n++eeTbtOpUyfKysqqvg8ZMoSVK1emrI2HImu7k6ruxFROO6A7MSIiIq1K1iYxNe/EZHEoREREWqGsvXIfHBPzcVSgOzEiItK8WuKr+ptTps8va5OYqjsxn0RJjMbEiIhIM8rLy2Pbtm0Zv9Cniruzbds28vLy6l85RbJ4YG9ETyeJiEgK9OnThw0bNrB169ZMNyVl8vLy6NOnT8aOn7VJTFV3ku7EiIhICuTm5nL88cdnuhltWlq7k8zsCDObb2blZvaOmY1twDYvmJmbWbvoewczuz/afreZ/d3Mzml0Wyo/fPJRVKAkRkREpDVJ952Ye4CPgF5AIfBHM1vh7quSrWxml1Kzje2Ad4GhwHrg68BjZjbI3dc1ukUv3RH+1NNJIiIirUrartxm1hkYA0x29z3u/grwe+CyWtbvDvw3cFO83N3L3X2Ku69z90/d/WngbeALTWrY7k3RAXUnRkREpDWxdI2aNrPTgEXu3jFWNhEY6u7nJVn/HmANMJ+QpOS6+4Ek6/UC3gEK3f31JPXFQHH0dSDwz2Y4HWm8o4APMt2ILKXYZ45in1mKf+ac5O5dU32QdHYndQF2JZTtAmqcpJkVAV8BrgdqHfZsZrnAr4GSZAkMgLvPAeZE6y9z96ImtV4OiWKfOYp95ij2maX4Z46ZLUvHcdI5EGQP0C2hrBuwO15gZjnAvcD1ye68JKw3lzDG5trmbaqIiIi0dOlMYt4A2plZv1jZ54HEQb3dgCLgUTN7H3g1Kt9gZkMAzMyA+wkDhMe4+8cpbbmIiIi0OGnrTnL3cjN7ErjVzK4mPJ10PvDlhFV3Afmx78cCSwkDdyvfGDQbOBk4y933NqIZc5rQdGkein3mKPaZo9hnluKfOWmJfdoG9kJ4TwzwADAc2Ab8wN0fMbPjgNXAKe6+PmGbAmIDe83ss8A6YD8Q724a7+6/Tv1ZiIiISEuQ1iRGREREpLnoDW8iIiLSKimJERERkVYpK5KYpszZJMnVN3eVmQ0zs9fNrMLMyqIxTJV1ZmZ3mNm2aPlx9KRZZX1BtE1FtI+z0n1+rYWZ9TOzfWb2cKxMsU8xM7vYzF6L/i1ZG3tiUrFPoShGz5jZDjN738zujs2np9g3IzO71syWmdl+M3sooS5lsTazsdF1pdzMfmdhDG393L3NL8A84FHCC/e+SngCakCm29UaF6AzMAUoICTB5xLe9VNAeDvmLuBbQB7wE2BJbNvxwP8RXmB4DGEw9zWx+sXAT4GOhCkqdgJHZ/qcW+IClAIvAw9H3xX71Md8OOHt4F+M/ts/JloU+9TH/hngoSi+nwFWAt9X7FMS628CFxCeAn4oVp6yWAMDCNeRMwjX6UeA3zSovZkOWBp+kM6EF+KdGCubC9ye6ba1lQX4R/QfZTFhaol47PcC/aPvi4DiWP1VlX8JgBMJT5x1jdW/HP9LoKUqLhcDjxGSycokRrFPfdwXAVclKVfsUx/714Cvx77/BPiVYp/SmE+nehKTslgDM4BHYnUnEK7bXetrZzZ0J50IfOLub8TKVhAyPzlEFuauOpHw0sIBhNgC4d1AwFoOxrpaPdV/hwHAW+6+u5Z6AcysG3ArcGNClWKfQmZ2GOElnEeb2Roz2xB1aXREsU+HnwMXm1knMzsGOAf4M4p9OqUy1on7Xkt086G+RmVDEtPgOZukcazm3FX1xTqxfhfQJeo31e/UMNOA+9393YRyxT61egG5wL8BQwgv6zwNuAXFPh1eIlzoPgQ2AMuA36HYp1MqY93k3yIbkpgGzdkkjWPJ566qL9aJ9d2APR7uH+p3qoeZFQJnAT9LUq3Yp1blm8F/4e6b3P0DQv/+11HsUyr6t+ZZ4ElCF8ZRQA/gDhT7dEplrJv8W2RDEtPQOZukgaLMOtncVasIsa1crzOhb3NVsnqq/w6rgL5m1rWWeoEzCQOo11uYV2wiMMbM/oZin1LuvoNwByDZ20EV+9Q6gjD9zN3uvt/dtwEPEhJIxT59UhnrxH33BToQrt91y/TgoTQNUPoN4QmlzsBX0NNJhxrPXwJLgC4J5UdHsR1DGL1+B9VHr19DGKB3DGF+rFVUH72+BLgz2nY0elIgMe6dCE9mVC53Ar+N4q7Ypz7+txImpO1JuBPwMqF7T7FPfezfAn5AmO/vcGA+oStbsW/+WLeL4vEjwt32vKgsZbHmYFfhEMJ1+mH0dFK1H+UIQv9pObAeGJvpNrXWBfgs4f9G9xFuAVYul0b1ZwGvE26/vwgUxLY14MfA9mj5MdHUF1F9QbTNXsKjemdl+nxb8kLs6STFPi3xzgXujf7xfR+YBeQp9mmJfWEUox3AB8DjQE/FPiWxnhL9Gx9fpqQ61sBYwvW5HHgKOKIh7dXcSSIiItIqZcOYGBEREWmDlMSIiIhIq6QkRkRERFolJTEiIiLSKimJERERkVZJSYyIiIi0SkpiRLKImT1kZk9nuh1xZna+mb1pZgfM7KEUHaPFnbeIHDolMSJpEl1I3cxuSSg/Myo/KlNty7D7gCcIL1K8PkXHuB4Ydyg7MLMrzWxPM7VHRJqBkhiR9NoH3GRmR2e6Ic0pmtG8KdsdTpjQ71l33+juiTPZNgt33+XuO1OxbxHJHCUxIulVBqwDJte2QrI7M2ZWEJUVJaxzjpn91cz2mtnLZtbHzIaa2Qoz22NmT5vZkUmOcYuZbY7WedDMOsbqzMxuMrO10X5Xmtm4JG25xMxeMLO9wPhazqWHmZWY2Y5oX38xswGV50B4jTzAC9E+z6xlP+3NbIaZvWNm+83sLTP7fqz+DDP7XzPbF53Xz8ysfay+WneSmb1oZvdG+/zAzLaY2Z3RjMlJfxPCpIOdo3a6mU2p7xyj+u5mNjc6xr6o7RNi9ePN7I2obquZPWtm7WL13zaz1VH9G2b2n/F21re9SFumJEYkvT4lTGR3jZmd0Az7mwpMAP6VMCnho8APgWLCrNcDCHOhxA0lzBg7jDCZ29mEydwqTQeuAr4HnEKYCO5XZjYqYT8/IswldAphbrJkHoradj7wL0AF8OcoaVoUtY+oHb2jsmRKgMuBG4CTo/btBDCzY4A/AX8HTovqLonaV5dLgQPAl4FrCXG8qJZ1F0X1FVE7exMms6vvHCHEcxBwLtAf+HdgY9T2IuAewu94EmFumj9XHtTM/gOYQfhNTwZuBP4L+G5Dthdp8zI92ZQWLdmyEC52T0efy4hmaSUkGw4clex7VFYQlRUlrDMits61UdnpsbIpwD8T2rCT2AzkhLEi+wmzx3YmTNA2JKHtdwHPJLTlxnrOt1+03hmxsu6EmXCvjr4fFa1zZgP2M7KW+tuANUBOrOzK6Jw6JcY++v4isDhhP88B99XRjiuBPU04x98DD9ayz29G63atpX49cFlC2QRgdUO216KlrS+65SiSGTcBS8zsznrXrNs/Yp83R3+uTCjrmbiNu8cHqC4G2gMnAB2APMKdhPjssLmEbrC4ZfW07WTCnafFlQXuvsvMVhLu3jTUadF+yuo4zmJ3/zRW9grhnD5H9RjFJZa/R81Y1ach5zgb+K2ZnU5IlP7g7i9Fdc8B7wBvm9mzQCnwpLvvjsZNHUu4CzY7dsx2hBmD69y+kech0iqpO0kkA9z9VcITOXckqa68GFusrLaBsx/HdxvtO7GsMX/PK9c9DyiMLQMI3U5x5fXsy+qo8zrqGrOfyvra9lfXcT5O+N7YWFUeuzaVv8efCE9e3Um48/RHM3swqtsNnA5cSLjrcjPwupnlx9pyDdV/i4FE3XD1bC/S5imJEcmcScAQYGRC+dboz96xssJmPO4gM+sc+/5F4CNgLbCa0A3zWXdfk7C808jjrCb8G/OlygIz60YYH7K6Efv5W7Sfr9VxnC8lDMr9KgfPqbl8BByW5Nj1nqO7f+Duc939SsKYnSvMrENUd8DdX3D3m4FTCV1657r7ZsLYmROS/BZrYvtOun0znrdIi6XuJJEMcfc1ZjaHmu9GWQO8C0wxsx8QxqDcQvNpBzxgZrcC+cDtwP+4ezlA1MV1p5kZsADoQkh0PnX3OQ09iLu/aWZPEbpDigljcW4DPgQeaeR+HgPuM7PrCUlNH6DA3ecSBhdPAO41s58DfaNzutvdKxp6nAZYB+SZ2XDCIOKKhpxjFOe/AasIsf8m8Ja77zezcwndeAuA7YRErSvwWnTMKcAvzGwn8AzhjtzpwDHu/qMGbC/SpulOjEhm3Up4QqZK1B10MeFivILw5MmkZjzmS4QLahkwH3iBMEan0mTCxXNitN5zhKeH3m7Csb4NLCUMbl0KdCIM0N3byP1cTkgKZgGvEwbqdgdw943AOYSxM8uBB4B5NG/McPdFwC+jfW/lYMzqO8f9hMRmBbCQkGScF9XtBC4A/hKd10TCgOCXo2PeR3ia6bJo+5cJT5693ZDtRdo6c29M17SIiIhIy6A7MSIiItIqKYkRERGRVklJjIiIiLRKSmJERESkVVISIyIiIq2SkhgRERFplZTEiIiISKukJEZERERapf8H/ihh0Lp5WfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the moons dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `solver=\"lbfgs\"`, `n_estimators=100`, and `gamma=\"scale\"` since these will be the default values in upcoming Scikit-Learn versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code examples:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "svm_clf = SVC(gamma=\"scale\", random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: the results in this notebook may differ slightly from the book, as Scikit-Learn algorithms sometimes get tweaked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Soft voting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(probability=True, random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "svm_clf = SVC(gamma=\"scale\", probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n",
      "VotingClassifier 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging and Pasting\n",
    "## Bagging and Pasting in Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–5. A single Decision Tree (left) versus a bagging ensemble of 500 trees (right):**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_without_and_with_bagging_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "plt.ylabel(\"\")\n",
    "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Bag evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8986666666666666"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(), n_estimators=500,\n",
    "    bootstrap=True, oob_score=True, random_state=40)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32275132, 0.67724868],\n",
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       "       [0.        , 1.        ],\n",
       "       [0.09497207, 0.90502793],\n",
       "       [0.31147541, 0.68852459],\n",
       "       [0.01754386, 0.98245614],\n",
       "       [0.97109827, 0.02890173],\n",
       "       [0.97765363, 0.02234637],\n",
       "       [0.74404762, 0.25595238],\n",
       "       [0.        , 1.        ],\n",
       "       [0.7173913 , 0.2826087 ],\n",
       "       [0.85026738, 0.14973262],\n",
       "       [0.97222222, 0.02777778],\n",
       "       [0.0625    , 0.9375    ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.97837838, 0.02162162],\n",
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       "       [0.99435028, 0.00564972],\n",
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       "       [0.9947644 , 0.0052356 ],\n",
       "       [0.00555556, 0.99444444],\n",
       "       [0.98963731, 0.01036269],\n",
       "       [0.26153846, 0.73846154],\n",
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       "       [0.        , 1.        ],\n",
       "       [0.80113636, 0.19886364],\n",
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       "       [0.0106383 , 0.9893617 ],\n",
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       "       [0.01036269, 0.98963731],\n",
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       "       [0.99453552, 0.00546448],\n",
       "       [0.01960784, 0.98039216],\n",
       "       [0.17857143, 0.82142857],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [0.98295455, 0.01704545],\n",
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       "       [0.00561798, 0.99438202],\n",
       "       [0.75690608, 0.24309392],\n",
       "       [0.38624339, 0.61375661],\n",
       "       [0.40625   , 0.59375   ],\n",
       "       [0.87368421, 0.12631579],\n",
       "       [0.92462312, 0.07537688],\n",
       "       [0.05181347, 0.94818653],\n",
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       "       [0.03804348, 0.96195652],\n",
       "       [0.02040816, 0.97959184],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.94915254, 0.05084746],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99462366, 0.00537634],\n",
       "       [0.        , 1.        ],\n",
       "       [0.39378238, 0.60621762],\n",
       "       [0.33152174, 0.66847826],\n",
       "       [0.00609756, 0.99390244],\n",
       "       [0.        , 1.        ],\n",
       "       [0.3172043 , 0.6827957 ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00588235, 0.99411765],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98924731, 0.01075269],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.62893082, 0.37106918],\n",
       "       [0.92344498, 0.07655502],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99526066, 0.00473934],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98888889, 0.01111111],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.06989247, 0.93010753],\n",
       "       [1.        , 0.        ],\n",
       "       [0.03608247, 0.96391753],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02185792, 0.97814208],\n",
       "       [1.        , 0.        ],\n",
       "       [0.95808383, 0.04191617],\n",
       "       [0.78362573, 0.21637427],\n",
       "       [0.56650246, 0.43349754],\n",
       "       [0.        , 1.        ],\n",
       "       [0.18023256, 0.81976744],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93121693, 0.06878307],\n",
       "       [0.97175141, 0.02824859],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00531915, 0.99468085],\n",
       "       [0.        , 1.        ],\n",
       "       [0.43010753, 0.56989247],\n",
       "       [0.85858586, 0.14141414],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00558659, 0.99441341],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96923077, 0.03076923],\n",
       "       [0.        , 1.        ],\n",
       "       [0.21649485, 0.78350515],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98477157, 0.01522843],\n",
       "       [0.8       , 0.2       ],\n",
       "       [0.99441341, 0.00558659],\n",
       "       [0.        , 1.        ],\n",
       "       [0.09497207, 0.90502793],\n",
       "       [0.99492386, 0.00507614],\n",
       "       [0.01714286, 0.98285714],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02747253, 0.97252747],\n",
       "       [1.        , 0.        ],\n",
       "       [0.77005348, 0.22994652],\n",
       "       [0.        , 1.        ],\n",
       "       [0.90229885, 0.09770115],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.22222222, 0.77777778],\n",
       "       [0.20348837, 0.79651163],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.20338983, 0.79661017],\n",
       "       [0.98181818, 0.01818182],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98969072, 0.01030928],\n",
       "       [0.        , 1.        ],\n",
       "       [0.48663102, 0.51336898],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00529101, 0.99470899],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.08379888, 0.91620112],\n",
       "       [0.12352941, 0.87647059],\n",
       "       [0.99415205, 0.00584795],\n",
       "       [0.03517588, 0.96482412],\n",
       "       [1.        , 0.        ],\n",
       "       [0.39790576, 0.60209424],\n",
       "       [0.05434783, 0.94565217],\n",
       "       [0.53191489, 0.46808511],\n",
       "       [0.51898734, 0.48101266],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.60869565, 0.39130435],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.24157303, 0.75842697],\n",
       "       [0.81578947, 0.18421053],\n",
       "       [0.08717949, 0.91282051],\n",
       "       [0.99453552, 0.00546448],\n",
       "       [0.82142857, 0.17857143],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.11904762, 0.88095238],\n",
       "       [0.04188482, 0.95811518],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.89150943, 0.10849057],\n",
       "       [0.19230769, 0.80769231],\n",
       "       [0.95238095, 0.04761905],\n",
       "       [0.00515464, 0.99484536],\n",
       "       [0.59375   , 0.40625   ],\n",
       "       [0.07692308, 0.92307692],\n",
       "       [0.99484536, 0.00515464],\n",
       "       [0.83684211, 0.16315789],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99484536, 0.00515464],\n",
       "       [0.95360825, 0.04639175],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.26395939, 0.73604061],\n",
       "       [0.98461538, 0.01538462],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00574713, 0.99425287],\n",
       "       [0.85142857, 0.14857143],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.75301205, 0.24698795],\n",
       "       [0.8969697 , 0.1030303 ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.75555556, 0.24444444],\n",
       "       [0.48863636, 0.51136364],\n",
       "       [0.        , 1.        ],\n",
       "       [0.92473118, 0.07526882],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87709497, 0.12290503],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.74752475, 0.25247525],\n",
       "       [0.09146341, 0.90853659],\n",
       "       [0.42268041, 0.57731959],\n",
       "       [0.22395833, 0.77604167],\n",
       "       [0.        , 1.        ],\n",
       "       [0.87046632, 0.12953368],\n",
       "       [0.78212291, 0.21787709],\n",
       "       [0.00507614, 0.99492386],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02884615, 0.97115385],\n",
       "       [0.96      , 0.04      ],\n",
       "       [0.93478261, 0.06521739],\n",
       "       [1.        , 0.        ],\n",
       "       [0.50731707, 0.49268293],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01604278, 0.98395722],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96987952, 0.03012048],\n",
       "       [0.        , 1.        ],\n",
       "       [0.05172414, 0.94827586],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99494949, 0.00505051],\n",
       "       [0.01675978, 0.98324022],\n",
       "       [1.        , 0.        ],\n",
       "       [0.14583333, 0.85416667],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00546448, 0.99453552],\n",
       "       [0.        , 1.        ],\n",
       "       [0.41836735, 0.58163265],\n",
       "       [0.13095238, 0.86904762],\n",
       "       [0.22110553, 0.77889447],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97647059, 0.02352941],\n",
       "       [0.21195652, 0.78804348],\n",
       "       [0.98882682, 0.01117318],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.96428571, 0.03571429],\n",
       "       [0.34554974, 0.65445026],\n",
       "       [0.98235294, 0.01764706],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99465241, 0.00534759],\n",
       "       [0.        , 1.        ],\n",
       "       [0.06043956, 0.93956044],\n",
       "       [0.98214286, 0.01785714],\n",
       "       [1.        , 0.        ],\n",
       "       [0.03108808, 0.96891192],\n",
       "       [0.58854167, 0.41145833]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Random Forest is equivalent to a bag of decision trees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n",
    "    n_estimators=500, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # very similar predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11249225099876375\n",
      "sepal width (cm) 0.02311928828251033\n",
      "petal length (cm) 0.4410304643639577\n",
      "petal width (cm) 0.4233579963547682\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure overlays the decision boundaries of 15 decision trees. As you can see, even though each decision tree is imperfect, the ensemble defines a pretty good decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(15):\n",
    "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
    "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
    "    tree_clf.fit(X_train[indices_with_replacement], y_train[indices_with_replacement])\n",
    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–6. MNIST pixel importance (according to a Random Forest classifier):**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n",
    "mnist.target = mnist.target.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = mpl.cm.hot,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mnist_feature_importance_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEYCAYAAACtEtpmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWgklEQVR4nO3dfbBdVX3G8ecJxFISQiQiNLxFBLQZXzIUB7UyorYSWixWrYoKSkVFRxBHcLTFii/gqFMGGFsZCqgRivG1ikixDEWHF63B8YU6WiAmBFJCQkyAhIiQ1T/OvvZwSbJ/l6yV3PPL9zNzh3vPfc7a+9xcznPW3mfd7VKKAADYkinbewcAAJMfZQEA6EVZAAB6URYAgF6UBQCgF2UBAOhFWQDAFtje3/aDtnfa3vuyPVEWACY929fY/ugmbj/W9j22d2617VLKnaWU6aWUR1ttI8r2HNul1uO1faTtuyJZygLAKPi8pONte9ztx0u6vJTySHSglsXS0vbeb8oCwCj4N0l7SDpi7AbbT5Z0jKQFtqfY/oDtO2zfZ/vLtvfocmOvxt9q+05J19m+yvYpwxuw/TPbrxy/4fGv5m1fb/vjtm/qDk9daXuW7ctt32/7R7bnDN2/2D7V9mLbq2x/2vaU7ntTbJ9pe6nte20vsL375vZb0ve7Ydd0236B7afbvq573Ku6/Zg5tP0ltk/vHt9a2wtt72J7mqSrJc3uxnrQ9uzN/QN4S3/uY5rN3wIBdjDrShn/6n3C5s+fX1atWjWh+9xyyy3/LWnD0E0XlVIuGvvC9r9o8Jx1Uvf1OyS9s5Qyz/Zpkl4v6TWSVkq6QNKMUspx3RP3ryV9UdI7JW2U9ApJ7yulHN6N9VwNnoz/qJTy8PB+Dd1/ainlEdvXS9pX0lGSVkm6WdLOkt4l6XpJl0p6tJRyYnf/0t3+aknTJV0r6VOllItt/62kv5P0ckn3SlogaV0p5fjN7Pdew/vSjX+QpKdpUCQzJH1N0o9LKad131/Sjf3K7ud7o6TzSykX2j5S0mWllH239G+j7gECQFWrVq3SokWLJnQf2xtKKYdtIfIFSVfZPqWU8pCkE7rbJOkdkt5dSrmrG+ssSXfaPn7o/meVUtZ13/+mpAttH1xKuU2Dw1kLxxfFFnyulHJHN9bVkuaWUq7tvv6KpI+Ny3+ylLJa0mrb50k6TtLFkt4o6dxSyuLuvh+UdKvtEzez34/bkVLK7ZJu775caftcSR8eF7uglLK8G+NKSfOCj/P3KAsADRRJ4dMIsRFLucH2SknH2v4vSc+T9Kru2wdI+obtjUN3eVSDV+Jjlg2N9VvbX5b0Jtsf0eDJ+zUT2J0VQ58/tImvp4/LLxv6fKmkscM9s7uvh7+38+b2e1NsP1WDmdQRknbT4PTCb8bF7hn6fP3Q9sM4ZwGgkUcm+BGyQIMZxfGSvltKGXuSXibp6FLKzKGPXUopdw/dd/xh9S9o8Mr+ZZLWl1JunuADnIj9hj7fX9Ly7vPlGhTd8Pce0WPLp2zm8zGf6G5/TillhqQ3SYoeSgyfaqAsADQwNrNoUhZ/Jult+v9DUJJ0oaSzbR8gSbb3tH3sFvdwUA4bJf2jBucFWjrD9pNt7yfpPZIWdrdfIem9tp9me7qkczQ4HLa5H8jKbp8PHLptN0kPanDSex9JZ0xgv1ZImjV2Un1LKAsADbQpi1LKEkk3SZom6VtD3zq/+/q7th+Q9ANJhweGXCDp2ZIuC+3AE/dNSbdI+omkqyRd0t1+qQZF9X0NTlxvkHTKJu4vSSqlrJd0tqQbba+x/XxJH5F0qKS13dhfj+5UKeWXGhTW4m483g0FIKbGu6EOO2xeWbTo2gndx97zlp4T3NXZPkHS20spL2q4jSLp4O5E9MjiBDeABuqf4K7N9q4avN31n7f3vowCDkMBaKDZOYsqbB+lwfH/FZL+dZtufEQxswDQyOSdWZRSrtHgvMe22NZWH9abDCgLAA0UDZY5IAvKAkADk/+cBSaGsgDQAGWRDWUBoBHKIhPKAkADzCyyoSwANEBZZENZAGiAssiGsgDQAGWRDWWxDexUaZyplbYVWbYfGWdDf0S/C2S2Jd75vy1RFplQFgAaYGaRDWUBoAHKIhvKAkADlEU2lAWABiiLbCgLAI1QFplQFgAaYGaRDWUBoAHKIhvKAkADXM8iG8piK0UWr+0SyEQWys0MZGYHMpF93i2QeUYgc2Ig8+1KmcjP+eeBzG8DGSm2KHHHfbpkZpENZQGgEcoiE8oCQAPMLLKhLAA0QFlkQ1kAaICyyIayANAAZZENZQGgEcoiE8oCQAPMLLKhLAA0QFlkQ1lsRvTqdpGFYJEFbgcEMrMCmYMCmSMCmQMDme8EMp8KZCKL2yLuCWSeFMhEr+4X+bd/uOL2RgtlkQ1lAaAByiIbygJAI5RFJpQFgAaYWWRDWQBoYKPqnY3CZEBZAGhkx/2buxlRFgAa4DBUNpQFgAYoi2woCwANUBbZpCuLyGK6qYHMrsHt7R3IPC+QiRzdfWkg87rIg4usAAycm3zmssBP6VXreyOPfqN/mEv7I7o9kFkWyCwJZKTY6dsHApmVgczoLdyjLLJJVxYAJgPKIhvKAkAjlEUmlAWABphZZENZAGiAssiGsgDQAGWRDWUBoAHKIhvKAkAjlEUmlAWABphZZJOuLKYEMpEFd5GroEmxq+AdFshErkw3O5A5J7B66w2B1WtzTghs7P39C+7uDyy4+2pgU5Er3O0eyEQW0kXWLErSikAmsgiw1rYm15/toyyySVcWACYDyiIbygJAA5RFNpQFgEYm14ExbB3KAkADzCyyoSwANEBZZENZAGiAssiGsgDQCGWRCWUBoAFmFtmMVFlEroIXWbwVGWdmIBPNRa5yNjeQWRTIzIlkpgdCzwhk/qI/MuPm/syLb+jPrOmPhK5wt1cgszqQkWKLANcFMvcFMpHFhNH93jYoi2xGqiwAjArKIhvKAkAbhXUWmVAWANrYuL13ADVRFgDqK2IBdzKUBYD6KIt0KAsAbXAYKhXKAkB9zCzSoSwAtMHMIhXKAkB9zCzSGamyiFwyNSJyydSpwbEi/z/8MJB5VqVMZKWv9glkAquqNS2QeaDOMHsEMq8LZL4XyCwOZKTYiunI6v1ZgczyQGbSoSxSGamyADAiijgMlQxlAaANZhapUBYA6uOcRTqUBYA2OAyVCmUBoD5mFulQFgDaYGaRCmUBoD5mFulQFgDqoyzSGamyiMxqI4vpIgulIgv3JOnwQCZw9VHNOyEQCqwou3tpYJzItT5/HsgE3PPT/sze+wYGCqxcWxvY1gsDm7otkJGk2wOZXQOZyIK70GLLyYbDUKmMVFkAGBHMLNKhLAC0QVmkQlkAqI8/95EOZQGgDWYWqVAWAOpjZpEOZQGgDWYWqVAWAOrj3VDpUBYA2uAwVCojVRaRK+VFXsxExokuylsRyNwTGeiAQOaY/sgD/9Sf+cFd/ZnI458XWAG5d2SVZORJJXCpvDmBf9gfBLb13P6IpNgV9X4RyAQuJjh6z7vMLNIZqbIAMEIoi1QoCwD18W6odCgLAG0ws0iFsgBQHzOLdCgLAG0ws0iFsgBQH++GSoeyANAGh6FSoSwA1MfMIp0dsix2C2RmB8c6NpB56VMCoTMDmWX9kWcEFuVFrgR3yBmB0KH9kfuP689sCFwq7qmv7c/c+5/9ma/2R0K/H1LsKolLApmVgczIPe9SFunskGUBYBvgMFQqlAWA+phZpENZAGiDmUUqlAWA+phZpENZAGiDskiFsgBQH3/uIx3KAkAbzCxSoSwA1MfMIp10ZRG5wts+gcys4PbmRkLrA5k/qbMxv7o/c8jawLbOCWQ+1B9ZHRhmTuAqeLq4P7IoMMxOgczBgYwUW9w4LThWSswsUklXFgAmAd4NlQ5lAaANDkOlQlkAqI+ZRTqUBYD6KIt0KAsAbXAYKhXKAkB9zCzSoSwAtMHMIhXKAkB9zCzSSVcWkd/PhwOZ6JXyfhnI7P2sQOj9gczNgcyUQOYNgczOpT+z3r2RyHo7fak/csfL+zM/DmxqSSBzdyAjSW8OZG4KjpUSZZFKurIAMAnw5z7SoSwAtMHMIhXKAkB9nLNIh7IA0AaHoVKhLADUx8wiHcoCQBvMLFKhLADUx8wiHcoCQBuURSrpyiJyJbTI1fR2C27vyL8KhI4KZE4LZJ4dyKwMZD4ZyPxH/4I7XdcfmXFyYFt/fmIg9LnexAOBUSLmBXPfC2Qi/xwRkd/rSfXczDqLdNKVBYBJYlK1F7YWZQGgPs5ZpENZAGiDw1CpUBYA6mNmkQ5lAaA+TnCnQ1kAaIOZRSqUBYD6OAyVDmUBoA0OQ6UyUmUR+d2bGsg8KZA5JpCRpDu/1Z/Z/0WBgQ4NZG4IZF4SyPih/syUP+zPHB7Y1kWBzNw6C+52D2QODGQOCmQk6bOBzO8CmciCu8g4kwozi3RGqiwAjIiiEWw4bAllAaANZhapUBYA6uOts+lQFgDaYGaRCmUBoD5OcKdDWQBog8NQqVAWAOpjZpEOZQGgPsoinZEqiymVxnltILMmOFZopv2LQCZy+b6jA5m3BzL/G1hwt7o/8pNL+jMz+yPa79T+zJLAOJHFbZHFfdcGMlLsKnjrg2OlxGGoVEaqLACMCGYW6VAWANpgZpEKZQGgPmYW6VAWANqgLFKhLADUx5/7SIeyANAGM4tUKAsA9XHOIh3KAkAbHIZKZaTKIrLoalogc0UgE1iTJkk6LhKKrATbLZCZFch8KJD53q79mbP7l5PdFthUJDMvkFleaVt7BjK3BjJRO+yLa2YW6YxUWQAYIcwsUqEsANTHzCIdygJAG5RFKpQFgPpYZ5EOZQGgDWYWqVAWAOrjnEU6lAWANjgMlQplAaAJJha5TJqyiCy4i2Qiv6AHBjIHBDJSbIHf0V/rz+z/2cBAvwtkvhPIHNq/4O66n/YPsziwqchFAiNrFl8cyESuShdZuBdYsigp9vsYuQDihuD2RglHofKZNGUBIBeOQuVCWQCojplFPpQFgCaYWeRCWQCojplFPpQFgOooi3woCwBNcBgqF8oCQHXMLPKhLAA0QVnkMlJl8QeBTGQR1JpA5pmBjCTNPzkQ2juQiez4yYf0Rh469X96MzcENvXjQOZXgUzkYUWuXjc/sFJufWBV3rLAtpYGMlJsvyNXXIystRy1J17+6Gw+I1UWAEbHqBUctoyyAFAdM4t8KAsATTCzyIWyAFAd74bKh7IA0ASHoXKhLABUx8wiH8oCQHWURT6UBYAmOAyVy0iVReSXL7LA6dmBTPjqZfvUGWzdif2Zh0/sX3C3NrA7kcV0kZ/jXoHMtEAmsnDvR4EFdzcFxlkZyET2R5KWB3M7ImYW+YxUWQAYHcwscqEsAFTHzCIfygJAE5RFLpQFgOr4cx/5UBYAmmBmkQtlAaA6zlnkQ1kAaILDULlQFgCqY2aRz6Qpi8gvVmSh3JRA5ppA5t2BjCTpvv7IQ+f1ZyJXpovs98xAZkkgE/lZRxbuTQ1k1gQyPwxkIovpIlfKi1zdTpLuD2R+G8hkfVJlZpHLpCkLAHkws8iHsgDQBGWRC2UBoDrWWeRDWQBogplFLpQFgOo4Z5EPZQGgCQ5D5UJZAKiOmUU+lAWA6jjBnQ9lAaAJZha5jFRZRH75VgQykVc8nw1kJGnDef2Z4wLjRC5Rumcgs1MgMzuQiVwydGYgsziQiVzqNHBV1dA4DwQy0Uvq1lrlnvFJlcNQ+YxUWQAYHRyGyoWyAFAdM4t8KAsATVAWuVAWAKrj3VD5UBYAmmBmkQtlAaA6Zhb5UBYAmmBmkQtlAaA63g2Vz0iVRWRaG1kEtS6Q+XogI8UW3F0XyMwNZE4KZKadHAhdHcgEdujfA+PcENjUzwOZNYFMZMFdrcv3SjvugrsoDkPlMlJlAWA0MLPIh7IAUB1lkQ9lAaAJDkPlQlkAqI6ZRT6UBYAmmFnkQlkAqI6ZRT6UBYAmKItcKAsA1fHnPvJJVxaRVzORxVuRK8VJ0iWBzNRA5rBAJrJPB1/Yn/lFYJypS/szywLjRK5wF1ncFlkoF1lsWXMhHa+ct4yfTy7pygLA9sc5i3woCwBNcBgqF8oCQHXMLPKhLAA0wcwiF8oCQHXMLPKhLAA0QVnkQlkAqI51FvlQFgCaYGaRy0iVRa1fvsg4kQVeUmyx2N6BzE8DmZ0CmZsCmVpXlIuME9nnSCZ69bo+kccVWbiHLeOcRT4jVRYARgOHofKhLAA0wcwiF8oCQHXMLPKhLAA0wcwiF8oCQHWc4M6HsgDQBIehcqEsAFTHzCIfygJAdUWsV8kmXVlEXs1EMquD24ssKFsTHKvPtEAm8tgiV+57UiBTa3Ff5Gf4cCDDk9Pkwswil3RlAWD7462z+VAWAJpgZpELZQGgOk5w50NZAGiCw1C5UBYAqmNmkQ9lAaA6yiIfygJAExyGyoWyAFAdM4t8KIutVGsRYMSaSuMA2wIzi1woCwDVMbPIh7IA0ARlkQtlAaA6/txHPpQFgCaYWeQyZXvvAIB8xs5ZTORje7O9v+0HbUf+EPIOh7IA0MTGCX70sb3E9grb04ZuO8n29ZH9sX297ZM29/1Syp2llOmllO3eXbbn2C62qxz9sX2k7bu2ZgzKAkB1DWcWO0t6T9WdnWRqFURtlAWAJmrPLDqflnS67Zmb+qbtF9r+ke213X9f2N1+tqQjJH2mO9T0mU3c9zGv5ruZyMdt39Td50rbs2xfbvv+bvw5Q/cvtk+1vdj2Ktuftj2l+94U22faXmr7XtsLbO8+brtvtX2npOskfb8bdk237RfYfrrt62zf141/+fDPoZt5nW77Z93jX2h7l24mdrWk2d1YD9qeHf+RD2yxwdaV4okOCAAbpWvWSU+Z4N12sb1o6OuLSikXjcssknS9pNMlnTn8Ddt7SLpK0qmSrpD0N5Kusn1QKeXvbf+ppMtKKRdPYJ9eL+koSask3dx9vEvSmyVdKunDkk4cyv+1pMMkTZd0raRfSbpY0lu6j5dIulfSAkmfkXT80H1fLOmPNejOvST9WtLMUsoj3eM7SNInNCiSGZK+JuksSacNjfFaSfMlbZB0o6S3lFIutH1099j3ncBjf4xJOd0BMNpKKfMbDv8Pkm60ff642/9S0m2llC92X19h+1RJr5D0+Se4rc+VUu6QJNtXS5pbSrm2+/orkj42Lv/JUspqSattnyfpOA3K4o2Szi2lLO7u+0FJt9oeLpqzSinruu8/bkdKKbdLur37cqXtczUoq2EXlFKWd2NcKWneE3nQm8JhKAAjpZRyq6RvS/rAuG/NlrR03G1LJe2zFZtbMfT5Q5v4evq4/LJx2x473DN+35Zq8GJ9r83c93FsP9X2l2zfbft+SZfp8bO3e4Y+X7+J/XvCKAsAo+jDkt6mxxbBckkHjMvtL+nu7vOyDfZrv3HbXt59Pn7f9pf0iB5bPmUzn4/5RHf7c0opMyS9SVL0VMFWP3bKAsDI6Q7JLNTg/MSY70g6xPYbbO9s+3WS5mowC5EGT8wHNt61M2w/2fZ+Grxra2F3+xWS3mv7abanSzpH0sKx8xGbsFKDcxfD+7ubpAc1OOm9j6QzJrBfKyTNGjup/kRQFgBG1Ucl/X7NRSnlPknHSHqfpPskvV/SMaWUVV3kfEmvsf0b2xc02qdvSrpF0k80ONl+SXf7pZK+qMHJ6V9rcAL6lM0NUkpZL+lsDc7NrLH9fEkfkXSopLXd2F+P7lQp5ZcaFNbibrwJvxvKpWyLmRkA5Ga7SDq4m/Wkw8wCANCLsgAA9OIwFACgFzMLAEAvygIA0IuyAAD0oiwAAL0oCwBAr/8D6FW7Onq9lnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(rnd_clf.feature_importances_)\n",
    "\n",
    "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\n",
    "cbar.ax.set_yticklabels(['Not important', 'Very important'])\n",
    "\n",
    "save_fig(\"mnist_feature_importance_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting\n",
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–8. Decision boundaries of consecutive predictors:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = len(X_train)\n",
    "\n",
    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
    "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n",
    "    sample_weights = np.ones(m) / m\n",
    "    plt.sca(axes[subplot])\n",
    "    for i in range(5):\n",
    "        svm_clf = SVC(kernel=\"rbf\", C=0.2, gamma=0.6, random_state=42)\n",
    "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n",
    "        y_pred = svm_clf.predict(X_train)\n",
    "\n",
    "        r = sample_weights[y_pred != y_train].sum() / sample_weights.sum() # equation 7-1\n",
    "        alpha = learning_rate * np.log((1 - r) / r) # equation 7-2\n",
    "        sample_weights[y_pred != y_train] *= np.exp(alpha) # equation 7-3\n",
    "        sample_weights /= sample_weights.sum() # normalization step\n",
    "\n",
    "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
    "        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
    "    if subplot == 0:\n",
    "        plt.text(-0.75, -0.95, \"1\", fontsize=14)\n",
    "        plt.text(-1.05, -0.95, \"2\", fontsize=14)\n",
    "        plt.text(1.0, -0.95, \"3\", fontsize=14)\n",
    "        plt.text(-1.45, -0.5, \"4\", fontsize=14)\n",
    "        plt.text(1.36,  -0.95, \"5\", fontsize=14)\n",
    "    else:\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let create a simple quadratic dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's train a decision tree regressor on this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–9. In this depiction of Gradient Boosting, the first predictor (top left) is trained normally, then each consecutive predictor (middle left and lower left) is trained on the previous predictor’s residuals; the right column shows the resulting ensemble’s predictions:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gradient_boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try a gradient boosting regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
       "                          random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\n",
    "gbrt.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–10. GBRT ensembles with not enough predictors (left) and too many (right):**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\n",
    "gbrt_slow.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gbrt_learning_rate_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "save_fig(\"gbrt_learning_rate_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient Boosting with Early stopping:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=2, n_estimators=56, random_state=42)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "errors = [mean_squared_error(y_val, y_pred)\n",
    "          for y_pred in gbrt.staged_predict(X_val)]\n",
    "bst_n_estimators = np.argmin(errors) + 1\n",
    "\n",
    "gbrt_best = GradientBoostingRegressor(max_depth=2, n_estimators=bst_n_estimators, random_state=42)\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 7–11. Tuning the number of trees using early stopping:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_error = np.min(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure early_stopping_gbrt_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(1, len(errors) + 1), errors, \"b.-\")\n",
    "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n",
    "plt.plot([0, 120], [min_error, min_error], \"k--\")\n",
    "plt.plot(bst_n_estimators, min_error, \"ko\")\n",
    "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
    "plt.axis([0, 120, 0, 0.01])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.ylabel(\"Error\", fontsize=16)\n",
    "plt.title(\"Validation error\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "save_fig(\"early_stopping_gbrt_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Early stopping with some patience (interrupts training only after there's no improvement for 5 epochs):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n",
    "\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "for n_estimators in range(1, 120):\n",
    "    gbrt.n_estimators = n_estimators\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break  # early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum validation MSE: 0.002712853325235463\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum validation MSE:\", min_val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using XGBoost:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost\n",
    "except ImportError as ex:\n",
    "    print(\"Error: the xgboost library is not installed.\")\n",
    "    xgboost = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.00400040950714611\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
    "    xgb_reg.fit(X_train, y_train)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred) # Not shown\n",
    "    print(\"Validation MSE:\", val_error)           # Not shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:0.22834\n",
      "Will train until validation_0-rmse hasn't improved in 2 rounds.\n",
      "[1]\tvalidation_0-rmse:0.16224\n",
      "[2]\tvalidation_0-rmse:0.11843\n",
      "[3]\tvalidation_0-rmse:0.08760\n",
      "[4]\tvalidation_0-rmse:0.06848\n",
      "[5]\tvalidation_0-rmse:0.05709\n",
      "[6]\tvalidation_0-rmse:0.05297\n",
      "[7]\tvalidation_0-rmse:0.05129\n",
      "[8]\tvalidation_0-rmse:0.05155\n",
      "[9]\tvalidation_0-rmse:0.05211\n",
      "Stopping. Best iteration:\n",
      "[7]\tvalidation_0-rmse:0.05129\n",
      "\n",
      "Validation MSE: 0.0026308690413069744\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg.fit(X_train, y_train,\n",
    "                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)  # Not shown\n",
    "    print(\"Validation MSE:\", val_error)            # Not shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.1 ms ± 5.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.8 ms ± 351 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit GradientBoostingRegressor().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Voting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Load the MNIST data and split it into a training set, a validation set, and a test set (e.g., use 50,000 instances for training, 10,000 for validation, and 10,000 for testing)._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST dataset was loaded earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
    "    mnist.data, mnist.target, test_size=10000, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=10000, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Then train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n",
    "svm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\n",
    "mlp_clf = MLPClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the RandomForestClassifier(random_state=42)\n",
      "Training the ExtraTreesClassifier(random_state=42)\n",
      "Training the LinearSVC(max_iter=100, random_state=42, tol=20)\n",
      "Training the MLPClassifier(random_state=42)\n"
     ]
    }
   ],
   "source": [
    "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n",
    "for estimator in estimators:\n",
    "    print(\"Training the\", estimator)\n",
    "    estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9692, 0.9715, 0.859, 0.9639]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in estimators]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Next, try to combine them into an ensemble that outperforms them all on the validation set, using a soft or hard voting classifier._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_estimators = [\n",
    "    (\"random_forest_clf\", random_forest_clf),\n",
    "    (\"extra_trees_clf\", extra_trees_clf),\n",
    "    (\"svm_clf\", svm_clf),\n",
    "    (\"mlp_clf\", mlp_clf),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(named_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('extra_trees_clf',\n",
       "                              ExtraTreesClassifier(random_state=42)),\n",
       "                             ('svm_clf',\n",
       "                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n",
       "                             ('mlp_clf', MLPClassifier(random_state=42))])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9711"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9692, 0.9715, 0.859, 0.9639]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the SVM to see if performance improves. It is possible to remove an estimator by setting it to `None` using `set_params()` like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('extra_trees_clf',\n",
       "                              ExtraTreesClassifier(random_state=42)),\n",
       "                             ('svm_clf', None),\n",
       "                             ('mlp_clf', MLPClassifier(random_state=42))])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.set_params(svm_clf=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This updated the list of estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n",
       " ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n",
       " ('svm_clf', None),\n",
       " ('mlp_clf', MLPClassifier(random_state=42))]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it did not update the list of _trained_ estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RandomForestClassifier(random_state=42),\n",
       " ExtraTreesClassifier(random_state=42),\n",
       " LinearSVC(max_iter=100, random_state=42, tol=20),\n",
       " MLPClassifier(random_state=42)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can either fit the `VotingClassifier` again, or just remove the SVM from the list of trained estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "del voting_clf.estimators_[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's evaluate the `VotingClassifier` again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9735"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bit better! The SVM was hurting performance. Now let's try using a soft voting classifier. We do not actually need to retrain the classifier, we can just set `voting` to `\"soft\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf.voting = \"soft\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9693"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nope, hard voting wins in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Once you have found one, try it on the test set. How much better does it perform compared to the individual classifiers?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9706"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.voting = \"hard\"\n",
    "voting_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9645, 0.9691, 0.9624]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voting classifier only very slightly reduced the error rate of the best model in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Stacking Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Run the individual classifiers from the previous exercise to make predictions on the validation set, and create a new training set with the resulting predictions: each training instance is a vector containing the set of predictions from all your classifiers for an image, and the target is the image's class. Train a classifier on this new training set._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_val_predictions[:, index] = estimator.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5., 5., 5.],\n",
       "       [8., 8., 8., 8.],\n",
       "       [2., 2., 3., 2.],\n",
       "       ...,\n",
       "       [7., 7., 7., 7.],\n",
       "       [6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7.]], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\n",
    "rnd_forest_blender.fit(X_val_predictions, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could fine-tune this blender or try other types of blenders (e.g., an `MLPClassifier`), then select the best one using cross-validation, as always."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Congratulations, you have just trained a blender, and together with the classifiers they form a stacking ensemble! Now let's evaluate the ensemble on the test set. For each image in the test set, make predictions with all your classifiers, then feed the predictions to the blender to get the ensemble's predictions. How does it compare to the voting classifier you trained earlier?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_test_predictions[:, index] = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rnd_forest_blender.predict(X_test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9683"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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